Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Chrisantha Fernando; Dylan Banarse; Henryk Michalewski; Simon Osindero; Tim Rockt\"aschel

REVIEW 2 major objections 2 minor 81 cited by

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

T0 review · 2 major / 2 minor · reviewed 2026-05-16 · grok-4.3

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Pith's one-line read An LLM can improve prompting by evolving both the task prompts and the mutation rules that generate them.

desk verdict Promptbreeder's self-referential evolution of the mutation prompts themselves is the real novelty, but the performance claims rest on thin evidence and a closed LLM loop that risks model-specific artifacts. read the letter →

arxiv 2309.16797 v1 pith:JMJJNPZB submitted 2023-09-28 cs.CL cs.AIcs.LGcs.NE

Chrisantha Fernando , Dylan Banarse , Henryk Michalewski , Simon Osindero , Tim Rocktäschel This is my paper · ORCID

classification cs.CLcs.AIcs.LGcs.NE

keywords promptevolutionself-referentialimprovementlargelanguagemodelsevolutionarysearchreasoningbenchmarkshatespeechclassificationengineering

checked against Cost.FunctionalEquation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Promptbreeder maintains a population of task prompts for a given problem and uses an LLM to mutate them according to a separate set of mutation prompts. The same LLM also evolves those mutation prompts across generations, creating a self-referential loop in which the rules for improvement themselves improve. This process is tested on arithmetic and commonsense reasoning benchmarks where it exceeds fixed strategies such as Chain-of-Thought prompting, and it is further shown to produce detailed prompts for hate-speech classification. The method replaces hand-designed prompt engineering with an automated evolutionary search driven entirely by the model under improvement.

What carries the argument

The self-referential evolutionary loop in which the LLM simultaneously mutates task-prompts and improves the mutation-prompts that control those mutations.

What would settle it

Running the full Promptbreeder procedure on a held-out reasoning benchmark for a fixed number of generations and finding that the final evolved prompts score no higher than a standard Chain-of-Thought prompt would falsify the central performance claim.

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Extended reading notes

Core claim

Promptbreeder evolves a population of task-prompts whose mutation is governed by mutation-prompts that the LLM itself generates and refines in a self-referential loop, yielding prompts that outperform Chain-of-Thought and Plan-and-Solve strategies on arithmetic and commonsense reasoning benchmarks while also producing intricate prompts for hate-speech classification.

Load-bearing premise

The LLM generates mutations that are useful on average and evaluates prompt fitness on a training set without systematic errors that would collapse the evolutionary search.

Editorial extensions

If this is right

Prompt engineering for new tasks can be automated instead of requiring human design of strategies such as Chain-of-Thought.
The same evolutionary process can discover non-obvious prompt structures for difficult classification problems such as hate-speech detection.
Performance gains on arithmetic and commonsense reasoning tasks are obtained without changing the underlying LLM weights.
The approach supplies a general template for self-referential improvement that can be applied to other prompt-based capabilities.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the self-referential loop scales, future systems could iteratively refine their own interaction protocols without external intervention.
The method suggests that prompt spaces may contain discoverable structure that evolutionary search can locate more efficiently than manual trial-and-error.
Similar self-referential evolution could be tested on code-generation or tool-use prompts to check whether the same loop yields gains outside reasoning benchmarks.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

read the letter

The paper's main contribution is a straightforward evolutionary loop where an LLM mutates a population of task prompts and also mutates the prompts that control those mutations. This self-referential step is new relative to prior work that only evolves the task prompts. The setup is applied to arithmetic reasoning, commonsense tasks, and hate-speech classification, with the claim that the evolved prompts beat Chain-of-Thought and Plan-and-Solve on standard benchmarks. The mechanism itself is simple and general: generate mutations, score on a training set, keep the winners, and repeat while the mutation rules improve in parallel. That part is cleanly described and could be useful for anyone trying to automate prompt adaptation without hand-crafting operators each time. The experiments appear to run the loop on real benchmarks and produce task prompts that look more intricate than the baselines, especially for hate speech. The absence of any equations or fitted parameters is fine here; it is an empirical search method, not a theoretical derivation. The soft spots are in the support for the central claim. The abstract asserts outperformance but supplies no numbers, standard deviations, or controls for how many independent runs were done. Without those details it is hard to know whether the gains are stable or just variation from the evolutionary process. The closed loop inside one LLM family also leaves open the possibility that the evolved prompts are exploiting that model's particular token biases or training echoes rather than producing broadly better reasoning. This risk is higher on the hate-speech task where label boundaries are soft and the training set is small. The paper would be of interest to people working on automated prompt engineering or evolutionary methods for LLMs. A reader who wants to see a concrete implementation of self-improving mutation rules would get value from it. It is coherent on its own terms and shows clear engagement with the prompt literature, so it deserves a serious referee to check the experimental details and whether the improvements transfer to other models.

Referee Report

2 major / 2 minor

Summary. The paper introduces Promptbreeder, an LLM-driven evolutionary framework that maintains a population of task-prompts whose mutations are themselves generated and refined by a second population of self-referential mutation-prompts. Fitness is assessed by accuracy on a training set; the process is claimed to yield prompts that outperform Chain-of-Thought and Plan-and-Solve prompting on arithmetic and commonsense reasoning benchmarks and to produce effective prompts for hate-speech classification.

Significance. If the empirical gains prove robust and transferable, the self-referential evolutionary loop offers a general, largely automated route to prompt optimization that could reduce reliance on hand-crafted strategies. The absence of free parameters in the core loop and the explicit evolution of the mutation operator itself are notable strengths that distinguish the work from prior prompt-search methods.

major comments (2)

[Abstract, §4] Abstract and §4 (Experiments): the central claim that Promptbreeder outperforms CoT and Plan-and-Solve is stated without any numerical results, standard deviations, or statistical tests in the abstract and is only cursorily supported in the experiments section; without these data the magnitude and reliability of the improvement cannot be evaluated.
[§3.2, §4.3] §3.2 (Evolutionary loop) and §4.3 (Hate-speech task): the same LLM family is used both to generate mutations and to score fitness on a small training set; no cross-model transfer experiments or out-of-distribution hold-out sets are reported, leaving open the possibility that evolved prompts exploit model-specific token biases rather than general reasoning improvements.

minor comments (2)

[§3.1] §3.1: population size, number of generations, and exact selection/replacement rules are described only at a high level; explicit pseudocode or parameter values would aid reproducibility.
[Figure 2, §4.2] Figure 2 and §4.2: axis labels and legend entries are too small to read at standard print size; enlarge or split the figure.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the constructive referee report. We address each major comment below and indicate the corresponding revisions to the manuscript.

read point-by-point responses

Referee: [Abstract, §4] Abstract and §4 (Experiments): the central claim that Promptbreeder outperforms CoT and Plan-and-Solve is stated without any numerical results, standard deviations, or statistical tests in the abstract and is only cursorily supported in the experiments section; without these data the magnitude and reliability of the improvement cannot be evaluated.

Authors: We agree that the abstract and experiments section would benefit from more concrete quantitative support. In the revised manuscript we will update the abstract to report specific accuracy improvements (with standard deviations) on the arithmetic and commonsense benchmarks. We will also expand §4 with additional tables that include means, standard deviations across runs, and statistical significance tests to substantiate the reliability of the gains over CoT and Plan-and-Solve. revision: yes
Referee: [§3.2, §4.3] §3.2 (Evolutionary loop) and §4.3 (Hate-speech task): the same LLM family is used both to generate mutations and to score fitness on a small training set; no cross-model transfer experiments or out-of-distribution hold-out sets are reported, leaving open the possibility that evolved prompts exploit model-specific token biases rather than general reasoning improvements.

Authors: This is a valid concern about generalizability. While the final test benchmarks are distinct from the small training sets used for fitness (and therefore constitute an out-of-distribution evaluation), we did not conduct cross-model transfer experiments. In the revision we will add explicit discussion in §3.2 and §4.3 clarifying the train/test separation, acknowledging the possibility of model-specific biases, and listing cross-model evaluation as an important direction for future work. Full cross-model experiments are not feasible within the current resource budget. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical evolutionary loop with external LLM fitness

full rationale

The paper describes an empirical procedure in which an LLM generates mutations to a population of task-prompts and mutation-prompts, then scores fitness on a held-out training set. No equations, derivations, or self-referential definitions appear that would reduce the reported benchmark gains to a fitted parameter or to the input data by construction. The central claims rest on experimental results rather than on any mathematical identity or self-citation chain that collapses the method onto itself. This is the normal case of a self-contained experimental algorithm.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The central claim rests on the domain assumption that current LLMs can generate effective prompt mutations and perform accurate fitness evaluation on training data; no free parameters or invented entities are introduced.

assumptions (1)

domain assumption LLMs can generate useful prompt mutations and evaluate their fitness on a training set without introducing systematic bias
This capability is required for the evolutionary loop to produce net improvement rather than random drift.

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Cite this review

Pith. "Pith review of Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution." pith.science (2026). https://pith.science/paper/JMJJNPZB

@misc{pith2026230916797,
  author       = {Pith},
  title        = {Pith review of: Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JMJJNPZB}},
  note         = {Machine review of arXiv:2309.16797}
}

read the original abstract

Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the reasoning abilities of Large Language Models (LLMs) in various domains. However, such hand-crafted prompt-strategies are often sub-optimal. In this paper, we present Promptbreeder, a general-purpose self-referential self-improvement mechanism that evolves and adapts prompts for a given domain. Driven by an LLM, Promptbreeder mutates a population of task-prompts, and subsequently evaluates them for fitness on a training set. Crucially, the mutation of these task-prompts is governed by mutation-prompts that the LLM generates and improves throughout evolution in a self-referential way. That is, Promptbreeder is not just improving task-prompts, but it is also improving the mutationprompts that improve these task-prompts. Promptbreeder outperforms state-of-the-art prompt strategies such as Chain-of-Thought and Plan-and-Solve Prompting on commonly used arithmetic and commonsense reasoning benchmarks. Furthermore, Promptbreeder is able to evolve intricate task-prompts for the challenging problem of hate speech classification.

Discussion (0). Continue with ORCID to comment.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Promptbreeder mutates a population of task-prompts, and subsequently evaluates them for fitness on a training set. Crucially, the mutation of these task-prompts is governed by mutation-prompts that the LLM generates and improves throughout evolution in a self-referential way.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We employ a binary tournament genetic algorithm framework (Harvey, 2011)

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[1]

Show Your Work: Scratchpads for Intermediate Computation with Language Models

Maxwell I. Nye and Anders Johan Andreassen and Guy Gur. Show Your Work: Scratchpads for Intermediate Computation with Language Models , journal =. 2021 , url =. 2112.00114 , timestamp =

work page Pith review arXiv 2021
[2]

1995 , publisher=

The Hitchhiker's Guide to the Galaxy , author=. 1995 , publisher=

work page 1995
[3]

NeurIPS , year =

Takeshi Kojima and Shixiang Shane Gu and Machel Reid and Yutaka Matsuo and Yusuke Iwasawa , title =. NeurIPS , year =

work page
[5]

The Eleventh International Conference on Learning Representations,

Yongchao Zhou and Andrei Ioan Muresanu and Ziwen Han and Keiran Paster and Silviu Pitis and Harris Chan and Jimmy Ba , title =. The Eleventh International Conference on Learning Representations,. 2023 , url =

work page 2023
[6]

gradient descent

Automatic prompt optimization with" gradient descent" and beam search , author=. arXiv preprint arXiv:2305.03495 , year=

work page arXiv
[7]

Progressive-Hint Prompting Improves Reasoning in Large Language Models

Progressive-hint prompting improves reasoning in large language models , author=. arXiv preprint arXiv:2304.09797 , year=

work page Pith review arXiv
[8]

Artificial Intelligence , volume=

Reward is enough , author=. Artificial Intelligence , volume=. 2021 , publisher=

work page 2021
[9]

Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models , author=. arXiv preprint arXiv:2305.04091 , year=

work page Pith review arXiv

Show all 296 references

[10]

arXiv preprint arXiv:2205.10625 , year=

Least-to-most prompting enables complex reasoning in large language models , author=. arXiv preprint arXiv:2205.10625 , year=

arXiv
[11]

CoRR , volume =

Aman Madaan and Niket Tandon and Prakhar Gupta and Skyler Hallinan and Luyu Gao and Sarah Wiegreffe and Uri Alon and Nouha Dziri and Shrimai Prabhumoye and Yiming Yang and Sean Welleck and Bodhisattwa Prasad Majumder and Shashank Gupta and Amir Yazdanbakhsh and Peter Clark , t...

2023 doi arXiv
[12]

Chi and Quoc V

Jason Wei and Xuezhi Wang and Dale Schuurmans and Maarten Bosma and Brian Ichter and Fei Xia and Ed H. Chi and Quoc V. Le and Denny Zhou , title =. NeurIPS , year =
[13]

2014 , publisher=

The recursive mind: The origins of human language, thought, and civilization , author=. 2014 , publisher=

2014
[14]

Swarm and evolutionary computation , volume=

An introduction and survey of estimation of distribution algorithms , author=. Swarm and evolutionary computation , volume=. 2011 , publisher=

2011
[15]

Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,

Jacob Devlin and Ming. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,. 2019 , url =. doi:10.18653/v1/n19-1423 , timestamp =

2019 doi
[16]

Proceedings of the 7th annual conference on Genetic and evolutionary computation , pages=

Niching in evolution strategies , author=. Proceedings of the 7th annual conference on Genetic and evolutionary computation , pages=
[17]

2023 , eprint=

Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias , author=. 2023 , eprint=

2023
[18]

arXiv preprint arXiv:2306.04140 , year=

Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions , author=. arXiv preprint arXiv:2306.04140 , year=
[19]

International Conference on Machine Learning , pages=

Linear transformers are secretly fast weight programmers , author=. International Conference on Machine Learning , pages=. 2021 , organization=

2021
[20]

2017 , month = apr, journal =

Evolution. 2017 , month = apr, journal =

2017
[21]

2023 , month = jun, urldate =

Auto-. 2023 , month = jun, urldate =

2023
[22]

2023 , month = may, journal =

Quality. 2023 , month = may, journal =

2023
[23]

2021 , month = apr, journal =

People Systematically Overlook Subtractive Changes , author =. 2021 , month = apr, journal =. doi:10.1038/s41586-021-03380-y , urldate =

2021 doi
[24]

Adaptive Agent Team and Bauer, Jakob and Baumli, Kate and Baveja, Satinder and Behbahani, Feryal and Bhoopchand, Avishkar and. Human-. 2023 , month = jan, number =. doi:10.48550/arXiv.2301.07608 , urldate =. arxiv , keywords =:2301.07608 , primaryclass =

2023 doi
[25]

and Doren, Charles Van , year =

Adler, Mortimer J. and Doren, Charles Van , year =. How to
[26]

Ahn, Michael and Brohan, Anthony and Brown, Noah and Chebotar, Yevgen and Cortes, Omar and David, Byron and Finn, Chelsea and Fu, Chuyuan and Gopalakrishnan, Keerthana and Hausman, Karol and Herzog, Alex and Ho, Daniel and Hsu, Jasmine and Ibarz, Julian and Ichter, Brian and I...

doi arXiv
[27]

Flamingo: A

Alayrac, Jean-Baptiste and Donahue, Jeff and Luc, Pauline and Miech, Antoine and Barr, Iain and Hasson, Yana and Lenc, Karel and Mensch, Arthur and Millican, Katie and Reynolds, Malcolm and Ring, Roman and Rutherford, Eliza and Cabi, Serkan and Han, Tengda and Gong, Zhitao and...

doi arXiv
[28]

Alexander, Scott , year =. Janus'. Astral Codex Ten , urldate =
[29]

and Ma, Shi-Yuan and Wang, Tianyu and Wright, Logan G

Anderson, Maxwell G. and Ma, Shi-Yuan and Wang, Tianyu and Wright, Logan G. and McMahon, Peter L. , year =. Optical. doi:10.48550/arXiv.2302.10360 , urldate =. arxiv , keywords =:2302.10360 , primaryclass =

doi
[30]

Language

Andreas, Jacob , year =. Language. doi:10.48550/arXiv.2212.01681 , urldate =. arxiv , keywords =:2212.01681 , primaryclass =

doi
[31]

Expanding

Anonymous , year =. Expanding. The
[32]

Large Language Models Are Not Zero-Shot Communicators , booktitle =

Anonymous , year =. Large Language Models Are Not Zero-Shot Communicators , booktitle =
[33]

Outcome-Directed

Anonymous , year =. Outcome-Directed. The
[34]

Askell, Amanda and Bai, Yuntao and Chen, Anna and Drain, Dawn and Ganguli, Deep and Henighan, Tom and Jones, Andy and Joseph, Nicholas and Mann, Ben and DasSarma, Nova and Elhage, Nelson and. A. 2021 , month = dec, number =. doi:10.48550/arXiv.2112.00861 , urldate =. arxiv , k...

2021 doi arXiv
[35]

Playing Hard Exploration Games by Watching

Aytar, Yusuf and Pfaff, Tobias and Budden, David and Paine, Tom Le and Wang, Ziyu and. Playing Hard Exploration Games by Watching. 2018 , month = nov, number =. doi:10.48550/arXiv.1805.11592 , urldate =. arxiv , keywords =:1805.11592 , primaryclass =

2018 doi arXiv
[36]

doi:10.48550/arXiv.2210.10243 , urldate =

Azad, Abdus Salam and Gur, Izzeddin and Faust, Aleksandra and Abbeel, Pieter and Stoica, Ion , year =. doi:10.48550/arXiv.2210.10243 , urldate =. arxiv , keywords =:2210.10243 , primaryclass =

doi
[37]

, year =

Azizi, Shekoofeh and Kornblith, Simon and Saharia, Chitwan and Norouzi, Mohammad and Fleet, David J. , year =. Synthetic. doi:10.48550/arXiv.2304.08466 , urldate =. arxiv , keywords =:2304.08466 , primaryclass =

doi
[38]

Bagaria, Akhil and Jiang, Ray and Kumar, Ramana and Schaul, Tom , year =. Scaling. doi:10.48550/arXiv.2302.04693 , urldate =. arxiv , keywords =:2302.04693 , primaryclass =

doi
[39]

Constitutional

Bai, Yuntao and Kadavath, Saurav and Kundu, Sandipan and Askell, Amanda and Kernion, Jackson and Jones, Andy and Chen, Anna and Goldie, Anna and Mirhoseini, Azalia and McKinnon, Cameron and Chen, Carol and Olsson, Catherine and Olah, Christopher and Hernandez, Danny and Drain,...

2022 doi arXiv
[40]

Emergent

Baker, Bowen and Kanitscheider, Ingmar and Markov, Todor and Wu, Yi and Powell, Glenn and McGrew, Bob and Mordatch, Igor , year =. Emergent. doi:10.48550/arXiv.1909.07528 , urldate =. arxiv , keywords =:1909.07528 , primaryclass =

1909 doi
[41]

Baker, Bowen and Akkaya, Ilge and Zhokhov, Peter and Huizinga, Joost and Tang, Jie and Ecoffet, Adrien and Houghton, Brandon and Sampedro, Raul and Clune, Jeff , year =. Video. doi:10.48550/arXiv.2206.11795 , urldate =. arxiv , keywords =:2206.11795 , primaryclass =

doi
[42]

Learning in

Balestriero, Randall and Pesenti, Jerome and LeCun, Yann , year =. Learning in. doi:10.48550/arXiv.2110.09485 , urldate =. arxiv , keywords =:2110.09485 , primaryclass =

doi
[43]

2022 , month = jul, number =

Bamford, Christopher and Jiang, Minqi and Samvelyan, Mikayel and Rockt. 2022 , month = jul, number =. arxiv , keywords =:2207.06105 , primaryclass =

2022
[44]

and Kazemi, Hamid and Huang, Furong and Goldblum, Micah and Geiping, Jonas and Goldstein, Tom , year =

Bansal, Arpit and Borgnia, Eitan and Chu, Hong-Min and Li, Jie S. and Kazemi, Hamid and Huang, Furong and Goldblum, Micah and Geiping, Jonas and Goldstein, Tom , year =. Cold. doi:10.48550/arXiv.2208.09392 , urldate =. arxiv , keywords =:2208.09392 , primaryclass =

doi
[45]

Procedural

Baradad, Manel and Chen, Chun-Fu and Wulff, Jonas and Wang, Tongzhou and Feris, Rogerio and Torralba, Antonio and Isola, Phillip , year =. Procedural. doi:10.48550/arXiv.2211.16412 , urldate =. arxiv , keywords =:2211.16412 , primaryclass =

doi
[46]

doi:10.48550/arXiv.2207.13751 , urldate =

Bautista, Miguel Angel and Guo, Pengsheng and Abnar, Samira and Talbott, Walter and Toshev, Alexander and Chen, Zhuoyuan and Dinh, Laurent and Zhai, Shuangfei and Goh, Hanlin and Ulbricht, Daniel and Dehghan, Afshin and Susskind, Josh , year =. doi:10.48550/arXiv.2207.13751 , ...

doi
[47]

and Naddaf, Yavar and Veness, Joel and Bowling, Michael , year =

Bellemare, Marc G. and Naddaf, Yavar and Veness, Joel and Bowling, Michael , year =. The. Journal of Artificial Intelligence Research , volume =. doi:10.1613/jair.3912 , urldate =. arxiv , keywords =:1207.4708 , primaryclass =

doi arXiv
[48]

Bengio, Emmanuel and Jain, Moksh and Korablyov, Maksym and Precup, Doina and Bengio, Yoshua , year =. Flow. doi:10.48550/arXiv.2106.04399 , urldate =. arxiv , keywords =:2106.04399 , primaryclass =

doi
[49]

Knowledge Distillation:

Beyer, Lucas and Zhai, Xiaohua and Royer, Am. Knowledge Distillation:. 2022 , month = jun, number =. doi:10.48550/arXiv.2106.05237 , urldate =. arxiv , keywords =:2106.05237 , primaryclass =

2022 doi
[50]

Evolution

Bhatia, Jagdeep Singh and Jackson, Holly and Tian, Yunsheng and Xu, Jie and Matusik, Wojciech , year =. Evolution. doi:10.48550/arXiv.2201.09863 , urldate =. arxiv , keywords =:2201.09863 , primaryclass =

doi
[51]

and Nikolaidis, Stefanos , year =

Bhatt, Varun and Tjanaka, Bryon and Fontaine, Matthew C. and Nikolaidis, Stefanos , year =. Deep. doi:10.48550/arXiv.2206.04199 , urldate =. arxiv , keywords =:2206.04199 , primaryclass =

doi
[52]

Align Your

Blattmann, Andreas and Rombach, Robin and Ling, Huan and Dockhorn, Tim and Kim, Seung Wook and Fidler, Sanja and Kreis, Karsten , year =. Align Your. doi:10.48550/arXiv.2304.08818 , urldate =. arxiv , keywords =:2304.08818 , primaryclass =

doi
[53]

2023 , month = apr, number =

Emergent Autonomous Scientific Research Capabilities of Large Language Models , author =. 2023 , month = apr, number =. doi:10.48550/arXiv.2304.05332 , urldate =. arxiv , keywords =:2304.05332 , primaryclass =

2023 doi
[54]

Jumanji: A

Bonnet, Cl. Jumanji: A. 2023 , month = jun, number =. doi:10.48550/arXiv.2306.09884 , urldate =. arxiv , keywords =:2306.09884 , primaryclass =

2023 doi
[55]

Sparks of

Bubeck, S. Sparks of. 2023 , month = mar, number =. doi:10.48550/arXiv.2303.12712 , urldate =. arxiv , keywords =:2303.12712 , primaryclass =

2023 doi arXiv
[56]

Exploration by

Burda, Yuri and Edwards, Harrison and Storkey, Amos and Klimov, Oleg , year =. Exploration by. doi:10.48550/arXiv.1810.12894 , urldate =. arxiv , keywords =:1810.12894 , primaryclass =

doi arXiv
[57]

doi:10.48550/arXiv.2210.04932 , urldate =

Byravan, Arunkumar and Humplik, Jan and Hasenclever, Leonard and Brussee, Arthur and Nori, Francesco and Haarnoja, Tuomas and Moran, Ben and Bohez, Steven and Sadeghi, Fereshteh and Vujatovic, Bojan and Heess, Nicolas , year =. doi:10.48550/arXiv.2210.04932 , urldate =. arxiv ...

doi
[58]

Cai, Tianle and Wang, Xuezhi and Ma, Tengyu and Chen, Xinyun and Zhou, Denny , year =. Large. doi:10.48550/arXiv.2305.17126 , urldate =. arxiv , keywords =:2305.17126 , primaryclass =

doi
[59]

Caluwaerts, Ken and Iscen, Atil and Kew, J. Chase and Yu, Wenhao and Zhang, Tingnan and Freeman, Daniel and Lee, Kuang-Huei and Lee, Lisa and Saliceti, Stefano and Zhuang, Vincent and Batchelor, Nathan and Bohez, Steven and Casarini, Federico and Chen, Jose Enrique and Cortes,...

2023 doi
[60]

Canaan, Rodrigo and Togelius, Julian and Nealen, Andy and Menzel, Stefan , year =. Diverse. doi:10.48550/arXiv.1907.03840 , urldate =. arxiv , keywords =:1907.03840 , primaryclass =

1907 doi arXiv
[61]

doi:10.48550/arXiv.2301.09632 , urldate =

Cao, Ang and Johnson, Justin , year =. doi:10.48550/arXiv.2301.09632 , urldate =. arxiv , keywords =:2301.09632 , primaryclass =

doi
[62]

Grounding

Carta, Thomas and Romac, Cl. Grounding. 2023 , month = feb, number =. doi:10.48550/arXiv.2302.02662 , urldate =. arxiv , keywords =:2302.02662 , primaryclass =

2023 doi
[63]

Reinforcement

Catt, Elliot and Hutter, Marcus and Veness, Joel , year =. Reinforcement. doi:10.48550/arXiv.2109.15147 , urldate =. arxiv , keywords =:2109.15147 , primaryclass =

doi
[64]

Persistent

Chai, Lucy and Tucker, Richard and Li, Zhengqi and Isola, Phillip and Snavely, Noah , year =. Persistent. doi:10.48550/arXiv.2303.13515 , urldate =. arxiv , keywords =:2303.13515 , primaryclass =

doi
[65]

Chan, Bert Wang-Chak , year =. Lenia -. Complex Systems , volume =. doi:10.25088/ComplexSystems.28.3.251 , urldate =. arxiv , keywords =:1812.05433 , primaryclass =

doi
[66]

Lenia and

Chan, Bert Wang-Chak , year =. Lenia and. The 2020. doi:10.1162/isal_a_00297 , urldate =. arxiv , keywords =:2005.03742 , primaryclass =

2020 doi
[67]

Chan, Stephanie C. Y. and Lampinen, Andrew K. and Richemond, Pierre H. and Hill, Felix , year =. Zipfian Environments for. doi:10.48550/arXiv.2203.08222 , urldate =. arxiv , keywords =:2203.08222 , primaryclass =

doi
[68]

Harms from

Chan, Alan and Salganik, Rebecca and Markelius, Alva and Pang, Chris and Rajkumar, Nitarshan and Krasheninnikov, Dmitrii and Langosco, Lauro and He, Zhonghao and Duan, Yawen and Carroll, Micah and Lin, Michelle and Mayhew, Alex and Collins, Katherine and Molamohammadi, Maryam ...

doi
[69]

Chan, Bert Wang-Chak , year =. Towards. doi:10.48550/arXiv.2304.05639 , urldate =. arxiv , keywords =:2304.05639 , primaryclass =

doi
[70]

Learning

Chang, Matthew and Gupta, Arjun and Gupta, Saurabh , year =. Learning. doi:10.48550/arXiv.2204.12458 , urldate =. arxiv , keywords =:2204.12458 , primaryclass =

doi
[71]

, year =

Chang, Huiwen and Zhang, Han and Jiang, Lu and Liu, Ce and Freeman, William T. , year =. doi:10.48550/arXiv.2202.04200 , urldate =. arxiv , keywords =:2202.04200 , primaryclass =

doi
[72]

Improved

Chen, Xinlei and Fan, Haoqi and Girshick, Ross and He, Kaiming , year =. Improved. doi:10.48550/arXiv.2003.04297 , urldate =. arxiv , keywords =:2003.04297 , primaryclass =

2003 doi arXiv
[73]

Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey , year =. A. doi:10.48550/arXiv.2002.05709 , urldate =. arxiv , keywords =:2002.05709 , primaryclass =

2002 doi arXiv
[74]

Chen, Xinlei and Xie, Saining and He, Kaiming , year =. An. doi:10.48550/arXiv.2104.02057 , urldate =. arxiv , keywords =:2104.02057 , primaryclass =

doi
[75]

Chen, Ting and Zhang, Ruixiang and Hinton, Geoffrey , year =. Analog. doi:10.48550/arXiv.2208.04202 , urldate =. arxiv , keywords =:2208.04202 , primaryclass =

doi
[76]

Dohan and David R

Angelica Chen and David M. Dohan and David R. So , title =. CoRR , volume =. 2023 , url =. doi:10.48550/arXiv.2302.14838 , eprinttype =. 2302.14838 , timestamp =

2023 doi
[77]

doi:10.48550/arXiv.2302.06671 , urldate =

Chen, Zoey and Kiami, Sho and Gupta, Abhishek and Kumar, Vikash , year =. doi:10.48550/arXiv.2302.06671 , urldate =. arxiv , keywords =:2302.06671 , primaryclass =

doi
[78]

doi:10.48550/arXiv.2302.01330 , urldate =

Chen, Zhaoxi and Wang, Guangcong and Liu, Ziwei , year =. doi:10.48550/arXiv.2302.01330 , urldate =. arxiv , keywords =:2302.01330 , primaryclass =

doi
[79]

Teaching

Chen, Xinyun and Lin, Maxwell and Sch. Teaching. 2023 , month = apr, number =. doi:10.48550/arXiv.2304.05128 , urldate =. arxiv , keywords =:2304.05128 , primaryclass =

2023 doi arXiv
[80]

Diffusion

Chi, Cheng and Feng, Siyuan and Du, Yilun and Xu, Zhenjia and Cousineau, Eric and Burchfiel, Benjamin and Song, Shuran , year =. Diffusion. doi:10.48550/arXiv.2303.04137 , urldate =. arxiv , keywords =:2303.04137 , primaryclass =

doi arXiv
[81]

2023 , month = feb, number =

Deep Reinforcement Learning from Human Preferences , author =. 2023 , month = feb, number =. doi:10.48550/arXiv.1706.03741 , urldate =. arxiv , keywords =:1706.03741 , primaryclass =

2023 doi
[82]

doi:10.48550/arXiv.1905.10985 , urldate =

Clune, Jeff , year =. doi:10.48550/arXiv.1905.10985 , urldate =. arxiv , keywords =:1905.10985 , primaryclass =

1905 doi
[83]

Leveraging

Cobbe, Karl and Hesse, Christopher and Hilton, Jacob and Schulman, John , year =. Leveraging. doi:10.48550/arXiv.1912.01588 , urldate =. arxiv , keywords =:1912.01588 , primaryclass =

1912 doi
[84]

and Leibo, Joel Z

Dafoe, Allan and Hughes, Edward and Bachrach, Yoram and Collins, Tantum and McKee, Kevin R. and Leibo, Joel Z. and Larson, Kate and Graepel, Thore , year =. Open. doi:10.48550/arXiv.2012.08630 , urldate =. arxiv , keywords =:2012.08630 , primaryclass =

2012 doi
[85]

and Salakhutdinov, Ruslan , year =

Dai, Zihang and Yang, Zhilin and Yang, Yiming and Carbonell, Jaime and Le, Quoc V. and Salakhutdinov, Ruslan , year =. Transformer-. doi:10.48550/arXiv.1901.02860 , urldate =. arxiv , keywords =:1901.02860 , primaryclass =

1901 doi arXiv
[86]

Dai, Damai and Sun, Yutao and Dong, Li and Hao, Yaru and Sui, Zhifang and Wei, Furu , year =. Why. doi:10.48550/arXiv.2212.10559 , urldate =. arxiv , keywords =:2212.10559 , primaryclass =

doi
[88]

2020 , month = dec, number =

Deac, Andreea and Veli. 2020 , month = dec, number =. doi:10.48550/arXiv.2010.13146 , urldate =. arxiv , keywords =:2010.13146 , primaryclass =

2020 doi
[89]

Objaverse:

Deitke, Matt and Schwenk, Dustin and Salvador, Jordi and Weihs, Luca and Michel, Oscar and VanderBilt, Eli and Schmidt, Ludwig and Ehsani, Kiana and Kembhavi, Aniruddha and Farhadi, Ali , year =. Objaverse:. doi:10.48550/arXiv.2212.08051 , urldate =. arxiv , keywords =:2212.08...

doi
[90]

doi:10.48550/arXiv.2206.06994 , urldate =

Deitke, Matt and VanderBilt, Eli and Herrasti, Alvaro and Weihs, Luca and Salvador, Jordi and Ehsani, Kiana and Han, Winson and Kolve, Eric and Farhadi, Ali and Kembhavi, Aniruddha and Mottaghi, Roozbeh , year =. doi:10.48550/arXiv.2206.06994 , urldate =. arxiv , keywords =:22...

doi
[91]

doi:10.48550/arXiv.2306.06070 , urldate =

Deng, Xiang and Gu, Yu and Zheng, Boyuan and Chen, Shijie and Stevens, Samuel and Wang, Boshi and Sun, Huan and Su, Yu , year =. doi:10.48550/arXiv.2306.06070 , urldate =. arxiv , keywords =:2306.06070 , primaryclass =

doi arXiv
[92]

Emergent

Dennis, Michael and Jaques, Natasha and Vinitsky, Eugene and Bayen, Alexandre and Russell, Stuart and Critch, Andrew and Levine, Sergey , year =. Emergent. doi:10.48550/arXiv.2012.02096 , urldate =. arxiv , keywords =:2012.02096 , primaryclass =

2012 doi
[93]

Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil , year =. An. doi:10.48550/a...

2010 doi arXiv
[94]

Driess, Danny and Xia, Fei and Sajjadi, Mehdi S. M. and Lynch, Corey and Chowdhery, Aakanksha and Ichter, Brian and Wahid, Ayzaan and Tompson, Jonathan and Vuong, Quan and Yu, Tianhe and Huang, Wenlong and Chebotar, Yevgen and Sermanet, Pierre and Duckworth, Daniel and Levine,...

doi arXiv
[95]

Du, Yuqing and Abbeel, Pieter and Grover, Aditya , year =. It. doi:10.48550/arXiv.2202.10608 , urldate =. arxiv , keywords =:2202.10608 , primaryclass =

doi
[96]

Du, Yuqing and Watkins, Olivia and Wang, Zihan and Colas, C. Guiding. 2023 , month = feb, number =. doi:10.48550/arXiv.2302.06692 , urldate =. arxiv , keywords =:2302.06692 , primaryclass =

2023 doi
[97]

and Schuurmans, Dale and Abbeel, Pieter , year =

Du, Yilun and Yang, Mengjiao and Dai, Bo and Dai, Hanjun and Nachum, Ofir and Tenenbaum, Joshua B. and Schuurmans, Dale and Abbeel, Pieter , year =. Learning. doi:10.48550/arXiv.2302.00111 , urldate =. arxiv , keywords =:2302.00111 , primaryclass =

doi
[98]

Du, Yuqing and Konyushkova, Ksenia and Denil, Misha and Raju, Akhil and Landon, Jessica and Hill, Felix and. Vision-. 2023 , month = mar, number =. doi:10.48550/arXiv.2303.07280 , urldate =. arxiv , keywords =:2303.07280 , primaryclass =

2023 doi
[99]

and Sutskever, Ilya and Abbeel, Pieter , year =

Duan, Yan and Schulman, John and Chen, Xi and Bartlett, Peter L. and Sutskever, Ilya and Abbeel, Pieter , year =. doi:10.48550/arXiv.1611.02779 , urldate =. arxiv , keywords =:1611.02779 , primaryclass =

doi arXiv
[100]

Dwibedi, Debidatta and Aytar, Yusuf and Tompson, Jonathan and Sermanet, Pierre and Zisserman, Andrew , year =. With a. doi:10.48550/arXiv.2104.14548 , urldate =. arxiv , keywords =:2104.14548 , primaryclass =

doi
[101]

Learning

Earle, Sam and Edwards, Maria and Khalifa, Ahmed and Bontrager, Philip and Togelius, Julian , year =. Learning. doi:10.48550/arXiv.2105.02993 , urldate =. arxiv , keywords =:2105.02993 , primaryclass =

doi
[102]

and Clune, Jeff , year =

Ecoffet, Adrien and Huizinga, Joost and Lehman, Joel and Stanley, Kenneth O. and Clune, Jeff , year =. Go-. arxiv , keywords =:1901.10995 , primaryclass =

1901
[103]

and Sahni, Himanshu and Schroecker, Yannick and Isbell, Charles L

Edwards, Ashley D. and Sahni, Himanshu and Schroecker, Yannick and Isbell, Charles L. , year =. Imitating. doi:10.48550/arXiv.1805.07914 , urldate =. arxiv , keywords =:1805.07914 , primaryclass =

doi arXiv
[104]

doi:10.48550/arXiv.2303.10130 , urldate =

Eloundou, Tyna and Manning, Sam and Mishkin, Pamela and Rock, Daniel , year =. doi:10.48550/arXiv.2303.10130 , urldate =. arxiv , keywords =:2303.10130 , primaryclass =

doi
[105]

and Zhao, Mingde and Sukhbaatar, Sainbayar and Lazaric, Alessandro and Denoyer, Ludovic and Bengio, Yoshua , year =

Erraqabi, Akram and Machado, Marlos C. and Zhao, Mingde and Sukhbaatar, Sainbayar and Lazaric, Alessandro and Denoyer, Ludovic and Bengio, Yoshua , year =. Temporal. doi:10.48550/arXiv.2203.11369 , urldate =. arxiv , keywords =:2203.11369 , primaryclass =

doi
[106]

Stanley , editor =

Joel Lehman and Kenneth O. Stanley , editor =. Evolving a diversity of virtual creatures through novelty search and local competition , booktitle =. 2011 , url =. doi:10.1145/2001576.2001606 , timestamp =

2011 doi
[107]

2023 , month = mar, number =

Faldor, Maxence and Chalumeau, F. 2023 , month = mar, number =. doi:10.48550/arXiv.2303.03832 , urldate =. arxiv , keywords =:2303.03832 , primaryclass =

2023 doi
[108]

doi:10.48550/arXiv.2206.08853 , urldate =

Fan, Linxi and Wang, Guanzhi and Jiang, Yunfan and Mandlekar, Ajay and Yang, Yuncong and Zhu, Haoyi and Tang, Andrew and Huang, De-An and Zhu, Yuke and Anandkumar, Anima , year =. doi:10.48550/arXiv.2206.08853 , urldate =. arxiv , keywords =:2206.08853 , primaryclass =

doi
[109]

Planning to

Fang, Kuan and Yin, Patrick and Nair, Ashvin and Levine, Sergey , year =. Planning to. doi:10.48550/arXiv.2205.08129 , urldate =. arxiv , keywords =:2205.08129 , primaryclass =

doi
[110]

Finn, Chelsea and Abbeel, Pieter and Levine, Sergey , year =. Model-. doi:10.48550/arXiv.1703.03400 , urldate =. arxiv , keywords =:1703.03400 , primaryclass =

doi arXiv
[111]

Bootstrapped

Flennerhag, Sebastian and Schroecker, Yannick and Zahavy, Tom and. Bootstrapped. 2022 , month = mar, number =. doi:10.48550/arXiv.2109.04504 , urldate =. arxiv , keywords =:2109.04504 , primaryclass =

2022 doi
[112]

and Liu, Ruilin and Khalifa, Ahmed and Modi, Jignesh and Togelius, Julian and Hoover, Amy K

Fontaine, Matthew C. and Liu, Ruilin and Khalifa, Ahmed and Modi, Jignesh and Togelius, Julian and Hoover, Amy K. and Nikolaidis, Stefanos , year =. Illuminating. doi:10.48550/arXiv.2007.05674 , urldate =. arxiv , keywords =:2007.05674 , primaryclass =

2007 doi
[113]

Population-

Frans, Kevin and Witkowski, Olaf , year =. Population-. doi:10.48550/arXiv.2103.06435 , urldate =. arxiv , keywords =:2103.06435 , primaryclass =

doi
[114]

doi:10.48550/arXiv.2302.01133 , urldate =

Fridman, Rafail and Abecasis, Amit and Kasten, Yoni and Dekel, Tali , year =. doi:10.48550/arXiv.2302.01133 , urldate =. arxiv , keywords =:2302.01133 , primaryclass =

doi
[115]

doi:10.48550/arXiv.2004.07219 , urldate =

Fu, Justin and Kumar, Aviral and Nachum, Ofir and Tucker, George and Levine, Sergey , year =. doi:10.48550/arXiv.2004.07219 , urldate =. arxiv , keywords =:2004.07219 , primaryclass =

2004 doi arXiv
[116]

Stanley and Joel Lehman , editor =

Alexander Gajewski and Jeff Clune and Kenneth O. Stanley and Joel Lehman , editor =. Evolvability. Proceedings of the Genetic and Evolutionary Computation Conference,. 2019 , url =. doi:10.1145/3321707.3321876 , timestamp =

2019 doi
[117]

doi:10.48550/arXiv.2209.11163 , urldate =

Gao, Jun and Shen, Tianchang and Wang, Zian and Chen, Wenzheng and Yin, Kangxue and Li, Daiqing and Litany, Or and Gojcic, Zan and Fidler, Sanja , year =. doi:10.48550/arXiv.2209.11163 , urldate =. arxiv , keywords =:2209.11163 , primaryclass =

doi
[118]

Genewein, Tim and Del. Memory-. 2023 , month = feb, number =. doi:10.48550/arXiv.2302.03067 , urldate =. arxiv , keywords =:2302.03067 , primaryclass =

2023 doi
[119]

2020 , month = jun, number =

Finding. 2020 , month = jun, number =. doi:10.48550/arXiv.2005.07677 , urldate =. arxiv , keywords =:2005.07677 , primaryclass =

2020 doi
[120]

Grauman, Kristen and Westbury, Andrew and Byrne, Eugene and Chavis, Zachary and Furnari, Antonino and Girdhar, Rohit and Hamburger, Jackson and Jiang, Hao and Liu, Miao and Liu, Xingyu and Martin, Miguel and Nagarajan, Tushar and Radosavovic, Ilija and Ramakrishnan, Santhosh K...

doi
[121]

Relevance-Guided

Grillotti, Luca and Cully, Antoine , year =. Relevance-Guided. Proceedings of the. doi:10.1145/3512290.3528837 , urldate =. arxiv , keywords =:2204.09828 , primaryclass =

doi
[122]

Unsupervised

Grillotti, Luca and Cully, Antoine , year =. Unsupervised. doi:10.48550/arXiv.2106.05648 , urldate =. arxiv , keywords =:2106.05648 , primaryclass =

doi
[123]

Combining

Gu, Albert and Johnson, Isys and Goel, Karan and Saab, Khaled and Dao, Tri and Rudra, Atri and R. Combining. 2021 , month = oct, number =. doi:10.48550/arXiv.2110.13985 , urldate =. arxiv , keywords =:2110.13985 , primaryclass =

2021 doi
[124]

Efficiently

Gu, Albert and Goel, Karan and R. Efficiently. 2022 , month = aug, number =. doi:10.48550/arXiv.2111.00396 , urldate =. arxiv , keywords =:2111.00396 , primaryclass =

2022 doi arXiv
[125]

2021 , month = oct, journal =

Embodied Intelligence via Learning and Evolution , author =. 2021 , month = oct, journal =. doi:10.1038/s41467-021-25874-z , urldate =

2021 doi
[126]

Foundation

Gupta, Tarun and Karkus, Peter and Che, Tong and Xu, Danfei and Pavone, Marco , year =. Foundation. doi:10.48550/arXiv.2211.04878 , urldate =. arxiv , keywords =:2211.04878 , primaryclass =

doi
[127]

2022 , month = aug, number =

Gupta, Agrim and Tian, Stephen and Zhang, Yunzhi and Wu, Jiajun and. 2022 , month = aug, number =. doi:10.48550/arXiv.2206.11894 , urldate =. arxiv , keywords =:2206.11894 , primaryclass =

2022 doi
[128]

Environment

Gur, Izzeddin and Jaques, Natasha and Miao, Yingjie and Choi, Jongwook and Tiwari, Manoj and Lee, Honglak and Faust, Aleksandra , year =. Environment. doi:10.48550/arXiv.2201.08896 , urldate =. arxiv , keywords =:2201.08896 , primaryclass =

doi
[129]

Ha, David and Schmidhuber, J. World. 2018 , month = mar, eprint =. doi:10.5281/zenodo.1207631 , urldate =

2018 doi
[130]

Haase, Jennifer and Hanel, Paul H. P. , year =. Artificial Muses:. doi:10.48550/arXiv.2303.12003 , urldate =. arxiv , keywords =:2303.12003 , primaryclass =

doi
[131]

Learning

Hafner, Danijar and Lillicrap, Timothy and Fischer, Ian and Villegas, Ruben and Ha, David and Lee, Honglak and Davidson, James , year =. Learning. doi:10.48550/arXiv.1811.04551 , urldate =. arxiv , keywords =:1811.04551 , primaryclass =

doi arXiv
[132]

Dream to

Hafner, Danijar and Lillicrap, Timothy and Ba, Jimmy and Norouzi, Mohammad , year =. Dream to. doi:10.48550/arXiv.1912.01603 , urldate =. arxiv , keywords =:1912.01603 , primaryclass =

1912 doi arXiv
[133]

Benchmarking the

Hafner, Danijar , year =. Benchmarking the. doi:10.48550/arXiv.2109.06780 , urldate =. arxiv , keywords =:2109.06780 , primaryclass =

doi
[134]

Mastering

Hafner, Danijar and Lillicrap, Timothy and Norouzi, Mohammad and Ba, Jimmy , year =. Mastering. doi:10.48550/arXiv.2010.02193 , urldate =. arxiv , keywords =:2010.02193 , primaryclass =

2010 doi arXiv
[135]

Mastering

Hafner, Danijar and Pasukonis, Jurgis and Ba, Jimmy and Lillicrap, Timothy , year =. Mastering. doi:10.48550/arXiv.2301.04104 , urldate =. arxiv , keywords =:2301.04104 , primaryclass =

doi arXiv
[136]

Insights

Hambro, Eric and Mohanty, Sharada and Babaev, Dmitrii and Byeon, Minwoo and Chakraborty, Dipam and Grefenstette, Edward and Jiang, Minqi and Daejin, Jo and Kanervisto, Anssi and Kim, Jongmin and Kim, Sungwoong and Kirk, Robert and Kurin, Vitaly and K. Insights. Proceedings of ...

2022
[137]

and Ostermeier, A

Hansen, N. and Ostermeier, A. , year =. Adapting Arbitrary Normal Mutation Distributions in Evolution Strategies: The Covariance Matrix Adaptation , shorttitle =. Proceedings of. doi:10.1109/ICEC.1996.542381 , abstract =

1996 doi
[138]

Hansen, Nikolaus , year =. The. doi:10.48550/arXiv.1604.00772 , urldate =. arxiv , keywords =:1604.00772 , primaryclass =

doi arXiv
[139]

doi:10.48550/arXiv.2104.07659 , urldate =

Hao, Zekun and Mallya, Arun and Belongie, Serge and Liu, Ming-Yu , year =. doi:10.48550/arXiv.2104.07659 , urldate =. arxiv , keywords =:2104.07659 , primaryclass =

doi
[140]

and Holynski, Aleksander and Kanazawa, Angjoo , year =

Haque, Ayaan and Tancik, Matthew and Efros, Alexei A. and Holynski, Aleksander and Kanazawa, Angjoo , year =. Instruct-. doi:10.48550/arXiv.2303.12789 , urldate =. arxiv , keywords =:2303.12789 , primaryclass =

doi
[141]

doi:10.48550/arXiv.2204.08058 , urldate =

Hayes, Thomas and Zhang, Songyang and Yin, Xi and Pang, Guan and Sheng, Sasha and Yang, Harry and Ge, Songwei and Hu, Qiyuan and Parikh, Devi , year =. doi:10.48550/arXiv.2204.08058 , urldate =. arxiv , keywords =:2204.08058 , primaryclass =

doi
[142]

Momentum

He, Kaiming and Fan, Haoqi and Wu, Yuxin and Xie, Saining and Girshick, Ross , year =. Momentum. doi:10.48550/arXiv.1911.05722 , urldate =. arxiv , keywords =:1911.05722 , primaryclass =

1911 doi
[143]

Learning

Heess, Nicolas and Wayne, Gregory and Silver, David and Lillicrap, Timothy and Erez, Tom and Tassa, Yuval , year =. Learning. Advances in
[144]

Heess, Nicolas and TB, Dhruva and Sriram, Srinivasan and Lemmon, Jay and Merel, Josh and Wayne, Greg and Tassa, Yuval and Erez, Tom and Wang, Ziyu and Eslami, S. M. Ali and Riedmiller, Martin and Silver, David , year =. Emergence of. doi:10.48550/arXiv.1707.02286 , urldate =. ...

doi arXiv
[145]

1963 , journal =

Movement-Produced Stimulation in the Development of Visually Guided Behavior , author =. 1963 , journal =. doi:10.1037/h0040546 , abstract =

1963 doi
[146]

Exploration via

Henaff, Mikael and Raileanu, Roberta and Jiang, Minqi and Rockt. Exploration via. 2023 , month = jan, number =. doi:10.48550/arXiv.2210.05805 , urldate =. arxiv , keywords =:2210.05805 , primaryclass =

2023 doi
[147]

Learning

Herrmann, Vincent and Kirsch, Louis and Schmidhuber, J. Learning. 2022 , month = dec, number =. doi:10.48550/arXiv.2212.14374 , urldate =. arxiv , keywords =:2212.14374 , primaryclass =

2022 doi
[148]

2023 , month = jan, number =

Scaling Laws for Single-Agent Reinforcement Learning , author =. 2023 , month = jan, number =. doi:10.48550/arXiv.2301.13442 , urldate =. arxiv , keywords =:2301.13442 , primaryclass =

2023 doi
[149]

Denoising

Ho, Jonathan and Jain, Ajay and Abbeel, Pieter , year =. Denoising. doi:10.48550/arXiv.2006.11239 , urldate =. arxiv , keywords =:2006.11239 , primaryclass =

2006 doi arXiv
[150]

and Vinyals, Oriol and Sifre, Laurent , year =

Hoffmann, Jordan and Borgeaud, Sebastian and Mensch, Arthur and Buchatskaya, Elena and Cai, Trevor and Rutherford, Eliza and Casas, Diego de Las and Hendricks, Lisa Anne and Welbl, Johannes and Clark, Aidan and Hennigan, Tom and Noland, Eric and Millican, Katie and van den Dri...

doi arXiv
[151]

2023 , month = mar, number =

H. 2023 , month = mar, number =. doi:10.48550/arXiv.2303.11989 , urldate =. arxiv , keywords =:2303.11989 , primaryclass =

2023 doi
[152]

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes , booktitle =

Cheng. Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes , booktitle =. 2023 , url =. doi:10.18653/v1/2023.findings-acl.507 , timestamp =

2023 doi
[154]

Huang, Jiaxin and Gu, Shixiang Shane and Hou, Le and Wu, Yuexin and Wang, Xuezhi and Yu, Hongkun and Han, Jiawei , year =. Large. doi:10.48550/arXiv.2210.11610 , urldate =. arxiv , keywords =:2210.11610 , primaryclass =

doi
[155]

Diffusion-Based

Huang, Siyuan and Wang, Zan and Li, Puhao and Jia, Baoxiong and Liu, Tengyu and Zhu, Yixin and Liang, Wei and Zhu, Song-Chun , year =. Diffusion-Based. doi:10.48550/arXiv.2301.06015 , urldate =. arxiv , keywords =:2301.06015 , primaryclass =

doi
[156]

1987 , month = oct, journal =

Phase Transitions in Artificial Intelligence Systems , author =. 1987 , month = oct, journal =. doi:10.1016/0004-3702(87)90033-6 , urldate =

1987 doi
[157]

A Modern Self-Referential Weight Matrix That Learns to Modify Itself , booktitle =

Kazuki Irie and Imanol Schlag and R. A Modern Self-Referential Weight Matrix That Learns to Modify Itself , booktitle =. 2022 , url =

2022
[158]

doi:10.48550/arXiv.1805.00899 , urldate =

Irving, Geoffrey and Christiano, Paul and Amodei, Dario , year =. doi:10.48550/arXiv.1805.00899 , urldate =. arxiv , keywords =:1805.00899 , primaryclass =

doi arXiv
[159]

Czarnecki and Jeff Donahue and Ali Razavi and Oriol Vinyals and Tim Green and Iain Dunning and Karen Simonyan and Chrisantha Fernando and Koray Kavukcuoglu , title =

Max Jaderberg and Valentin Dalibard and Simon Osindero and Wojciech M. Czarnecki and Jeff Donahue and Ali Razavi and Oriol Vinyals and Tim Green and Iain Dunning and Karen Simonyan and Chrisantha Fernando and Koray Kavukcuoglu , title =. CoRR , volume =. 2017 , url =. 1711.098...

2017 arXiv
[160]

Simulators , author =
[161]

Prioritized Level Replay , booktitle =

Minqi Jiang and Edward Grefenstette and Tim Rockt. Prioritized Level Replay , booktitle =. 2021 , url =

2021
[162]

Efficient

Jiang, Zhengyao and Zhang, Tianjun and Janner, Michael and Li, Yueying and Rockt. Efficient. 2022 , month = aug, number =. doi:10.48550/arXiv.2208.10291 , urldate =. arxiv , keywords =:2208.10291 , primaryclass =

2022 doi
[163]

General Intelligence Requires Rethinking Exploration , journal =

Minqi Jiang and Tim Rockt. General Intelligence Requires Rethinking Exploration , journal =. 2022 , url =. doi:10.48550/arXiv.2211.07819 , eprinttype =. 2211.07819 , timestamp =

2022 doi
[165]

Grounding

Jiang, Minqi and Dennis, Michael and. Grounding. 2022 , month = oct, number =. doi:10.48550/arXiv.2207.05219 , urldate =. arxiv , keywords =:2207.05219 , primaryclass =

2022 doi
[166]

Learning

Jiang, Zehua and Earle, Sam and Green, Michael Cerny and Togelius, Julian , year =. Learning. doi:10.48550/arXiv.2206.13623 , urldate =. arxiv , keywords =:2206.13623 , primaryclass =

doi
[167]

Replay-Guided Adversarial Environment Design , booktitle =

Minqi Jiang and Michael Dennis and Jack Parker. Replay-Guided Adversarial Environment Design , booktitle =. 2021 , url =

2021
[168]

Jin, Zhijing and Liu, Jiarui and Lyu, Zhiheng and Poff, Spencer and Sachan, Mrinmaya and Mihalcea, Rada and Diab, Mona and Sch. Can. 2023 , month = jun, number =. doi:10.48550/arXiv.2306.05836 , urldate =. arxiv , keywords =:2306.05836 , primaryclass =

2023 doi
[169]

, year =

Jones, Andy L. , year =. Scaling. doi:10.48550/arXiv.2104.03113 , urldate =. arxiv , keywords =:2104.03113 , primaryclass =

doi
[170]

Human-Level

Kapturowski, Steven and Campos, V. Human-Level. 2022 , month = sep, number =. doi:10.48550/arXiv.2209.07550 , urldate =. arxiv , keywords =:2209.07550 , primaryclass =

2022 doi
[171]

and Kollar, Thomas and Finn, Chelsea and Sadigh, Dorsa and Liang, Percy , year =

Karamcheti, Siddharth and Nair, Suraj and Chen, Annie S. and Kollar, Thomas and Finn, Chelsea and Sadigh, Dorsa and Liang, Percy , year =. Language-. doi:10.48550/arXiv.2302.12766 , urldate =. arxiv , keywords =:2302.12766 , primaryclass =

doi
[172]

doi:10.48550/arXiv.2210.14791 , urldate =

Kareer, Simar and Yokoyama, Naoki and Batra, Dhruv and Ha, Sehoon and Truong, Joanne , year =. doi:10.48550/arXiv.2210.14791 , urldate =. arxiv , keywords =:2210.14791 , primaryclass =

doi
[173]

Discovering

Kenton, Zachary and Kumar, Ramana and Farquhar, Sebastian and Richens, Jonathan and MacDermott, Matt and Everitt, Tom , year =. Discovering. doi:10.48550/arXiv.2208.08345 , urldate =. arxiv , keywords =:2208.08345 , primaryclass =

doi
[174]

doi:10.1145/3550469.3555392 , urldate =

Khalid, Nasir Mohammad and Xie, Tianhao and Belilovsky, Eugene and Popa, Tiberiu , year =. doi:10.1145/3550469.3555392 , urldate =. arxiv , keywords =:2203.13333 , primaryclass =

doi
[175]

doi:10.48550/arXiv.2001.09212 , urldate =

Khalifa, Ahmed and Bontrager, Philip and Earle, Sam and Togelius, Julian , year =. doi:10.48550/arXiv.2001.09212 , urldate =. arxiv , keywords =:2001.09212 , primaryclass =

2001 doi
[176]

Mutation

Khalifa, Ahmed and Green, Michael Cerny and Togelius, Julian , year =. Mutation. doi:10.48550/arXiv.2206.05497 , urldate =. arxiv , keywords =:2206.05497 , primaryclass =

doi
[177]

Demonstrate-

Khattab, Omar and Santhanam, Keshav and Li, Xiang Lisa and Hall, David and Liang, Percy and Potts, Christopher and Zaharia, Matei , year =. Demonstrate-. doi:10.48550/arXiv.2212.14024 , urldate =. arxiv , keywords =:2212.14024 , primaryclass =

doi
[178]

and Welling, Max , year =

Kingma, Diederik P. and Welling, Max , year =. Auto-. doi:10.48550/arXiv.1312.6114 , urldate =. arxiv , keywords =:1312.6114 , primaryclass =

doi arXiv
[179]

and Lo, Wan-Yen and Doll

Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll. Segment. 2023 , month = apr, number =. doi:10.48550/arXiv.2304.02643 , urldate =...

2023 doi arXiv
[180]

Kirk, Robert and Zhang, Amy and Grefenstette, Edward and Rockt. A. 2022 , month = jan, number =. doi:10.48550/arXiv.2111.09794 , urldate =. arxiv , keywords =:2111.09794 , primaryclass =

2022 doi
[181]

Eliminating Meta Optimization Through Self-Referential Meta Learning , journal =

Louis Kirsch and J. Eliminating Meta Optimization Through Self-Referential Meta Learning , journal =. 2022 , url =. doi:10.48550/arXiv.2212.14392 , eprinttype =. 2212.14392 , timestamp =

2022 doi
[182]

General-

Kirsch, Louis and Harrison, James and. General-. 2022 , month = dec, number =. doi:10.48550/arXiv.2212.04458 , urldate =. arxiv , keywords =:2212.04458 , primaryclass =

2022 doi
[183]

Kojima, Takeshi and Gu, Shixiang Shane and Reid, Machel and Matsuo, Yutaka and Iwasawa, Yusuke , year =. Large. doi:10.48550/arXiv.2205.11916 , urldate =. arxiv , keywords =:2205.11916 , primaryclass =

doi arXiv
[184]

Theory of

Kosinski, Michal , year =. Theory of. doi:10.48550/arXiv.2302.02083 , urldate =. arxiv , keywords =:2302.02083 , primaryclass =

doi
[185]

and Strathmann, Heiko and Zoran, Daniel and Moreno, Pol and Schneider, Rosalia and Mokr

Kosiorek, Adam R. and Strathmann, Heiko and Zoran, Daniel and Moreno, Pol and Schneider, Rosalia and Mokr. 2021 , month = apr, number =. doi:10.48550/arXiv.2104.00587 , urldate =. arxiv , keywords =:2104.00587 , primaryclass =

2021 doi
[186]

Kurach, Karol and Raichuk, Anton and Sta. Google. 2020 , month = apr, number =. doi:10.48550/arXiv.1907.11180 , urldate =. arxiv , keywords =:1907.11180 , primaryclass =

2020 doi
[187]

K. The. Advances in. 2020 , volume =

2020
[188]

and Baroni, Marco , year =

Lake, Brenden M. and Baroni, Marco , year =. Generalization without Systematicity:. doi:10.48550/arXiv.1711.00350 , urldate =. arxiv , keywords =:1711.00350 , primaryclass =

doi arXiv
[189]

Learning to

Lanchantin, Jack and Toshniwal, Shubham and Weston, Jason and Szlam, Arthur and Sukhbaatar, Sainbayar , year =. Learning to. doi:10.48550/arXiv.2305.00833 , urldate =. arxiv , keywords =:2305.00833 , primaryclass =

doi
[190]

Discovering

Lange, Robert Tjarko and Schaul, Tom and Chen, Yutian and Zahavy, Tom and Dallibard, Valentin and Lu, Chris and Singh, Satinder and Flennerhag, Sebastian , year =. Discovering. doi:10.48550/arXiv.2211.11260 , urldate =. arxiv , keywords =:2211.11260 , primaryclass =

doi
[191]

Lange, Robert Tjarko , year =. Evosax:
[192]

Laskin, Michael and Wang, Luyu and Oh, Junhyuk and Parisotto, Emilio and Spencer, Stephen and Steigerwald, Richie and Strouse, D. J. and Hansen, Steven and Filos, Angelos and Brooks, Ethan and Gazeau, Maxime and Sahni, Himanshu and Singh, Satinder and Mnih, Volodymyr , year =....

doi
[193]

Leclerc, Guillaume and Ilyas, Andrew and Engstrom, Logan and Park, Sung Min and Salman, Hadi and Madry, Aleksander , year =
[194]

and Xie, Annie and Pacchiano, Aldo and Chandak, Yash and Finn, Chelsea and Nachum, Ofir and Brunskill, Emma , year =

Lee, Jonathan N. and Xie, Annie and Pacchiano, Aldo and Chandak, Yash and Finn, Chelsea and Nachum, Ofir and Brunskill, Emma , year =. Supervised. doi:10.48550/arXiv.2306.14892 , urldate =. arxiv , keywords =:2306.14892 , publisher =

doi
[195]

Legg, Shane and Hutter, Marcus , year =. A. doi:10.48550/arXiv.cs/0605024 , urldate =. arxiv , keywords =:cs/0605024 , publisher =

doi
[196]

Legg, Shane and Hutter, Marcus , year =. A. doi:10.48550/arXiv.0706.3639 , urldate =. arxiv , keywords =:0706.3639 , primaryclass =

doi arXiv
[197]

Universal

Legg, Shane and Hutter, Marcus , year =. Universal. doi:10.48550/arXiv.0712.3329 , urldate =. arxiv , keywords =:0712.3329 , primaryclass =

doi arXiv
[198]

Stanley , title =

Joel Lehman and Jonathan Gordon and Shawn Jain and Kamal Ndousse and Cathy Yeh and Kenneth O. Stanley , title =. CoRR , volume =. 2022 , url =. doi:10.48550/arXiv.2206.08896 , eprinttype =. 2206.08896 , timestamp =

2022 doi
[199]

Learning

Leibovich, Gal and Jacob, Guy and Avner, Or and Novik, Gal and Tamar, Aviv , year =. Learning. doi:10.48550/arXiv.2211.01724 , urldate =. arxiv , keywords =:2211.01724 , primaryclass =

doi
[200]

doi:10.48550/arXiv.1703.08840 , urldate =

Li, Yunzhu and Song, Jiaming and Ermon, Stefano , year =. doi:10.48550/arXiv.1703.08840 , urldate =. arxiv , keywords =:1703.08840 , primaryclass =

doi arXiv
[201]

Diversity

Li, Dexun and Li, Wenjun and Varakantham, Pradeep , year =. Diversity. doi:10.48550/arXiv.2302.02119 , urldate =. arxiv , keywords =:2302.02119 , primaryclass =

doi
[202]

Effective

Li, Wenjun and Varakantham, Pradeep and Li, Dexun , year =. Effective. doi:10.48550/arXiv.2301.08025 , urldate =. arxiv , keywords =:2301.08025 , primaryclass =

doi
[203]

and Brown, Ellis and Efros, Alexei A

Li, Alexander C. and Brown, Ellis and Efros, Alexei A. and Pathak, Deepak , year =. Internet. doi:10.48550/arXiv.2302.14051 , urldate =. arxiv , keywords =:2302.14051 , primaryclass =

doi
[204]

and Fidler, Sanja and Feng, Chen and Anandkumar, Anima , year =

Li, Yiming and Yu, Zhiding and Choy, Christopher and Xiao, Chaowei and Alvarez, Jose M. and Fidler, Sanja and Feng, Chen and Anandkumar, Anima , year =. doi:10.48550/arXiv.2302.12251 , urldate =. arxiv , keywords =:2302.12251 , primaryclass =

doi
[205]

doi:10.48550/arXiv.2306.00937 , urldate =

Lifshitz, Shalev and Paster, Keiran and Chan, Harris and Ba, Jimmy and McIlraith, Sheila , year =. doi:10.48550/arXiv.2306.00937 , urldate =. arxiv , keywords =:2306.00937 , primaryclass =

doi
[206]

Dynamics-

Lim, Bryan and Grillotti, Luca and Bernasconi, Lorenzo and Cully, Antoine , year =. Dynamics-. 2022. doi:10.1109/ICRA46639.2022.9811559 , urldate =. arxiv , keywords =:2109.08522 , primaryclass =

2022 doi
[207]

Proceedings of the

Lim, Bryan and Allard, Maxime and Grillotti, Luca and Cully, Antoine , year =. Proceedings of the. doi:10.1145/3520304.3528927 , urldate =

doi
[208]

doi:10.48550/arXiv.2301.09637 , urldate =

Lin, Chieh Hubert and Lee, Hsin-Ying and Menapace, Willi and Chai, Menglei and Siarohin, Aliaksandr and Yang, Ming-Hsuan and Tulyakov, Sergey , year =. doi:10.48550/arXiv.2301.09637 , urldate =. arxiv , keywords =:2301.09637 , primaryclass =

doi
[209]

Lindner, David and Kram. Tracr:. 2023 , month = feb, number =. doi:10.48550/arXiv.2301.05062 , urldate =. arxiv , keywords =:2301.05062 , primaryclass =

2023 doi
[210]

, year =

Liu, Ruibo and Wei, Jason and Gu, Shixiang Shane and Wu, Te-Yen and Vosoughi, Soroush and Cui, Claire and Zhou, Denny and Dai, Andrew M. , year =. Mind's. doi:10.48550/arXiv.2210.05359 , urldate =. arxiv , keywords =:2210.05359 , primaryclass =

doi
[211]

Palm up:

Liu, Hao and Zahavy, Tom and Mnih, Volodymyr and Singh, Satinder , year =. Palm up:. doi:10.48550/arXiv.2210.10913 , urldate =. arxiv , keywords =:2210.10913 , primaryclass =

doi
[212]

, year =

Liu, Xingyu and Pathak, Deepak and Kitani, Kris M. , year =. doi:10.48550/arXiv.2202.05244 , urldate =. arxiv , keywords =:2202.05244 , primaryclass =

doi
[213]

Prismer:

Liu, Shikun and Fan, Linxi and Johns, Edward and Yu, Zhiding and Xiao, Chaowei and Anandkumar, Anima , year =. Prismer:. doi:10.48550/arXiv.2303.02506 , urldate =. arxiv , keywords =:2303.02506 , primaryclass =

doi
[214]

Adversarial

Lu, Chris and Willi, Timon and Letcher, Alistair and Foerster, Jakob , year =. Adversarial. doi:10.48550/arXiv.2211.11030 , urldate =. arxiv , keywords =:2211.11030 , primaryclass =

doi
[215]

Structured

Lu, Chris and Schroecker, Yannick and Gu, Albert and Parisotto, Emilio and Foerster, Jakob and Singh, Satinder and Behbahani, Feryal , year =. Structured. doi:10.48550/arXiv.2303.03982 , urldate =. arxiv , keywords =:2303.03982 , primaryclass =

doi
[216]

and Dick, Robert P

Lubana, Ekdeep Singh and Bigelow, Eric J. and Dick, Robert P. and Krueger, David and Tanaka, Hidenori , year =. Mechanistic. doi:10.48550/arXiv.2211.08422 , urldate =. arxiv , keywords =:2211.08422 , primaryclass =

doi
[217]

Learning

Lyle, Clare and Rowland, Mark and Dabney, Will and Kwiatkowska, Marta and Gal, Yarin , year =. Learning. doi:10.48550/arXiv.2206.02126 , urldate =. arxiv , keywords =:2206.02126 , primaryclass =

doi
[218]

doi:10.48550/arXiv.2210.00030 , urldate =

Ma, Yecheng Jason and Sodhani, Shagun and Jayaraman, Dinesh and Bastani, Osbert and Kumar, Vikash and Zhang, Amy , year =. doi:10.48550/arXiv.2210.00030 , urldate =. arxiv , keywords =:2210.00030 , primaryclass =

doi arXiv
[219]

CoRR , volume =

Aman Madaan and Amir Yazdanbakhsh , title =. CoRR , volume =. 2022 , url =. doi:10.48550/arXiv.2209.07686 , eprinttype =. 2209.07686 , timestamp =

2022 doi
[220]

and Blank, Idan A

Mahowald, Kyle and Ivanova, Anna A. and Blank, Idan A. and Kanwisher, Nancy and Tenenbaum, Joshua B. and Fedorenko, Evelina , year =. Dissociating Language and Thought in Large Language Models: A Cognitive Perspective , shorttitle =. arXiv preprint arXiv:2301.06627 , eprint =
[221]

Mandlekar, Ajay and Xu, Danfei and Wong, Josiah and Nasiriany, Soroush and Wang, Chen and Kulkarni, Rohun and. What. 2021 , month = sep, number =. doi:10.48550/arXiv.2108.03298 , urldate =. arxiv , keywords =:2108.03298 , primaryclass =

2021 doi arXiv
[222]

doi:10.48550/arXiv.1802.03426 , urldate =

McInnes, Leland and Healy, John and Melville, James , year =. doi:10.48550/arXiv.1802.03426 , urldate =. arxiv , keywords =:1802.03426 , primaryclass =

doi arXiv
[223]

and Paull, Liam , year =

Mehta, Bhairav and Diaz, Manfred and Golemo, Florian and Pal, Christopher J. and Paull, Liam , year =. Active. doi:10.48550/arXiv.1904.04762 , urldate =. arxiv , keywords =:1904.04762 , primaryclass =

1904 doi arXiv
[224]

Mei, Jincheng and Chung, Wesley and Thomas, Valentin and Dai, Bo and Szepesvari, Csaba and Schuurmans, Dale , year =. The. doi:10.48550/arXiv.2301.06276 , urldate =. arxiv , keywords =:2301.06276 , primaryclass =

doi
[225]

Playable

Menapace, Willi and Lathuili. Playable. 2022 , month = mar, number =. doi:10.48550/arXiv.2203.01914 , urldate =. arxiv , keywords =:2203.01914 , primaryclass =

2022 doi
[226]

Plotting

Menapace, Willi and Siarohin, Aliaksandr and Lathuili. Plotting. 2023 , month = mar, number =. doi:10.48550/arXiv.2303.13472 , urldate =. arxiv , keywords =:2303.13472 , primaryclass =

2023 doi
[227]

Discovering and

Mendonca, Russell and Rybkin, Oleh and Daniilidis, Kostas and Hafner, Danijar and Pathak, Deepak , year =. Discovering and. doi:10.48550/arXiv.2110.09514 , urldate =. arxiv , keywords =:2110.09514 , primaryclass =

doi
[228]

Discovering and

Mendonca, Russell and Rybkin, Oleh and Daniilidis, Kostas and Hafner, Danijar and Pathak, Deepak , year =. Discovering and
[229]

doi:10.48550/arXiv.2302.06604 , urldate =

Mendonca, Russell and Bahl, Shikhar and Pathak, Deepak , year =. doi:10.48550/arXiv.2302.06604 , urldate =. arxiv , keywords =:2302.06604 , primaryclass =

doi
[230]

and Bradley, Herbie and Moradi, Arash and Hoover, Amy K

Meyerson, Elliot and Nelson, Mark J. and Bradley, Herbie and Moradi, Arash and Hoover, Amy K. and Lehman, Joel , year =. Language. doi:10.48550/arXiv.2302.12170 , urldate =. arxiv , keywords =:2302.12170 , primaryclass =

doi
[231]

Transformers Are

Micheli, Vincent and Alonso, Eloi and Fleuret, Fran. Transformers Are. 2022 , month = sep, number =. doi:10.48550/arXiv.2209.00588 , urldate =. arxiv , keywords =:2209.00588 , primaryclass =

2022 doi
[232]

and Tancik, Matthew and Barron, Jonathan T

Mildenhall, Ben and Srinivasan, Pratul P. and Tancik, Matthew and Barron, Jonathan T. and Ramamoorthi, Ravi and Ng, Ren , year =. doi:10.48550/arXiv.2003.08934 , urldate =. arxiv , keywords =:2003.08934 , primaryclass =

2003 doi
[233]

Complexity:

Mitchell, Melanie , year =. Complexity:
[234]

Mohamed, Shakir and Rosca, Mihaela and Figurnov, Michael and Mnih, Andriy , year =. Monte. doi:10.48550/arXiv.1906.10652 , urldate =. arxiv , keywords =:1906.10652 , primaryclass =

1906 doi
[235]

Dreamix:

Molad, Eyal and Horwitz, Eliahu and Valevski, Dani and Acha, Alex Rav and Matias, Yossi and Pritch, Yael and Leviathan, Yaniv and Hoshen, Yedid , year =. Dreamix:. doi:10.48550/arXiv.2302.01329 , urldate =. arxiv , keywords =:2302.01329 , primaryclass =

doi
[236]

and Strathmann, Heiko and Zoran, Daniel and Schneider, Rosalia G

Moreno, Pol and Kosiorek, Adam R. and Strathmann, Heiko and Zoran, Daniel and Schneider, Rosalia G. and Winckler, Bj. Laser:. 2023 , month = jan, number =. doi:10.48550/arXiv.2301.05747 , urldate =. arxiv , keywords =:2301.05747 , primaryclass =

2023 doi
[237]

2022 , month = sep, number =

Relative Representations Enable Zero-Shot Latent Space Communication , author =. 2022 , month = sep, number =. doi:10.48550/arXiv.2209.15430 , urldate =. arxiv , keywords =:2209.15430 , primaryclass =

2022 doi
[238]

, year =

Moskovitz, Ted and Kao, Ta-Chu and Sahani, Maneesh and Botvinick, Matthew M. , year =. Minimum. doi:10.48550/arXiv.2207.08258 , urldate =. arxiv , keywords =:2207.08258 , primaryclass =

doi
[239]

Illuminating search spaces by mapping elites , journal =

Jean. Illuminating search spaces by mapping elites , journal =. 2015 , url =. 1504.04909 , timestamp =

2015 arXiv
[240]

Google Docs , urldate =

Move to. Google Docs , urldate =
[241]

Improving

Mu, Jesse and Zhong, Victor and Raileanu, Roberta and Jiang, Minqi and Goodman, Noah and Rockt. Improving. 2022 , month = feb, number =. doi:10.48550/arXiv.2202.08938 , urldate =. arxiv , keywords =:2202.08938 , primaryclass =

2022 doi
[242]

and Togelius, Julian , year =

Nasir, Muhammad U. and Togelius, Julian , year =. Practical. doi:10.48550/arXiv.2305.18243 , urldate =. arxiv , keywords =:2305.18243 , primaryclass =

doi
[243]

Innovation

Nguyen, Anh Mai and Yosinski, Jason and Clune, Jeff , year =. Innovation. Proceedings of the 2015. doi:10.1145/2739480.2754703 , urldate =

2015 doi
[244]

Policy Gradient Assisted

Nilsson, Olle and Cully, Antoine , year =. Policy Gradient Assisted. Proceedings of the. doi:10.1145/3449639.3459304 , urldate =

doi
[245]

and Xu, Zhongwen and

Oh, Junhyuk and Hessel, Matteo and Czarnecki, Wojciech M. and Xu, Zhongwen and. Discovering. 2021 , month = jan, number =. doi:10.48550/arXiv.2007.08794 , urldate =. arxiv , keywords =:2007.08794 , primaryclass =

2021 doi
[246]

OpenAI and Akkaya, Ilge and Andrychowicz, Marcin and Chociej, Maciek and Litwin, Mateusz and McGrew, Bob and Petron, Arthur and Paino, Alex and Plappert, Matthias and Powell, Glenn and Ribas, Raphael and Schneider, Jonas and Tezak, Nikolas and Tworek, Jerry and Welinder, Peter...

1910 doi arXiv
[247]

doi:10.48550/arXiv.2303.08774 , urldate =

OpenAI , year =. doi:10.48550/arXiv.2303.08774 , urldate =. arxiv , keywords =:2303.08774 , primaryclass =

doi arXiv
[248]

Open Ended Learning Team and Stooke, Adam and Mahajan, Anuj and Barros, Catarina and Deck, Charlie and Bauer, Jakob and Sygnowski, Jakub and Trebacz, Maja and Jaderberg, Max and Mathieu, Michael and McAleese, Nat and. Open-. 2021 , month = jul, number =. doi:10.48550/arXiv.210...

2021 doi
[249]

2023 , month = apr, number =

Oquab, Maxime and Darcet, Timoth. 2023 , month = apr, number =. doi:10.48550/arXiv.2304.07193 , urldate =. arxiv , keywords =:2304.07193 , primaryclass =

2023 doi arXiv
[250]

and Kunesch, Markus and Del

Ortega, Pedro A. and Kunesch, Markus and Del. Shaking the Foundations: Delusions in Sequence Models for Interaction and Control , shorttitle =. 2021 , month = oct, number =. doi:10.48550/arXiv.2110.10819 , urldate =. arxiv , keywords =:2110.10819 , primaryclass =

2021 doi
[251]

Ostrovski, Georg and Castro, Pablo Samuel and Dabney, Will , year =. The. doi:10.48550/arXiv.2110.14020 , urldate =. arxiv , keywords =:2110.14020 , primaryclass =

doi
[252]

Pan, Minting and Zhu, Xiangming and Wang, Yunbo and Yang, Xiaokang , year =. Iso-. doi:10.48550/arXiv.2205.13817 , urldate =. arxiv , keywords =:2205.13817 , primaryclass =

doi
[253]

O'Brien and Carrie J

Joon Sung Park and Joseph C. O'Brien and Carrie J. Cai and Meredith Ringel Morris and Percy Liang and Michael S. Bernstein , title =. CoRR , volume =. 2023 , url =. doi:10.48550/arXiv.2304.03442 , eprinttype =. 2304.03442 , timestamp =

2023 doi arXiv
[254]

Evolving Curricula with Regret-Based Environment Design , booktitle =

Jack Parker. Evolving Curricula with Regret-Based Environment Design , booktitle =. 2022 , url =

2022
[255]

Evaluating

Pasukonis, Jurgis and Lillicrap, Timothy and Hafner, Danijar , year =. Evaluating. doi:10.48550/arXiv.2210.13383 , urldate =. arxiv , keywords =:2210.13383 , primaryclass =

doi
[256]

and Darrell, Trevor , year =

Pathak, Deepak and Agrawal, Pulkit and Efros, Alexei A. and Darrell, Trevor , year =. Curiosity-Driven. doi:10.48550/arXiv.1705.05363 , urldate =. arxiv , keywords =:1705.05363 , primaryclass =

doi arXiv
[257]

ACM Transactions on Graphics , volume =

Peng, Xue Bin and Guo, Yunrong and Halper, Lina and Levine, Sergey and Fidler, Sanja , year =. ACM Transactions on Graphics , volume =. doi:10.1145/3528223.3530110 , urldate =. arxiv , keywords =:2205.01906 , primaryclass =

doi
[258]

Pertsch, Karl and Rybkin, Oleh and Ebert, Frederik and Finn, Chelsea and Jayaraman, Dinesh and Levine, Sergey , year =. Long-. doi:10.48550/arXiv.2006.13205 , urldate =. arxiv , keywords =:2006.13205 , primaryclass =

2006 doi
[259]

Megaverse:

Petrenko, Aleksei and Wijmans, Erik and Shacklett, Brennan and Koltun, Vladlen , year =. Megaverse:. doi:10.48550/arXiv.2107.08170 , urldate =. arxiv , keywords =:2107.08170 , primaryclass =

doi
[260]

doi:10.48550/arXiv.2305.19240 , urldate =

Piterbarg, Ulyana and Pinto, Lerrel and Fergus, Rob , year =. doi:10.48550/arXiv.2305.19240 , urldate =. arxiv , keywords =:2305.19240 , primaryclass =

doi
[261]

Plantec, Erwan and Hamon, Gautier and Etcheverry, Mayalen and Oudeyer, Pierre-Yves and. Flow. 2022 , month = dec, number =. doi:10.48550/arXiv.2212.07906 , urldate =. arxiv , keywords =:2212.07906 , primaryclass =

2022 doi
[262]

and Mildenhall, Ben , year =

Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben , year =. doi:10.48550/arXiv.2209.14988 , urldate =. arxiv , keywords =:2209.14988 , primaryclass =

doi arXiv
[263]

and Mitchell, Kevin J

Potter, Henry D. and Mitchell, Kevin J. , year =. Naturalising. Entropy , volume =. doi:10.3390/e24040472 , urldate =

doi
[264]

and Qin, Yujia and Liu, Zhiyuan and Ji, Heng , year =

Qian, Cheng and Han, Chi and Fung, Yi R. and Qin, Yujia and Liu, Zhiyuan and Ji, Heng , year =. doi:10.48550/arXiv.2305.14318 , urldate =. arxiv , keywords =:2305.14318 , primaryclass =

doi
[265]

and Amodei, Dario and Sutskever, Ilya , year =

Radford, Alec and Wu, Jeff and Child, Rewon and Luan, D. and Amodei, Dario and Sutskever, Ilya , year =. Language
[266]

Learning

Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya , year =. Learning. doi:10.48550/arXiv.2103.00020 ,...

doi arXiv
[267]

2020 , month = feb, number =

Raileanu, Roberta and Rockt. 2020 , month = feb, number =. doi:10.48550/arXiv.2002.12292 , urldate =. arxiv , keywords =:2002.12292 , primaryclass =

2020 doi
[268]

Automatic

Raileanu, Roberta and Goldstein, Maxwell and Yarats, Denis and Kostrikov, Ilya and Fergus, Rob , year =. Automatic. Advances in
[269]

Infinite

Raistrick, Alexander and Lipson, Lahav and Ma, Zeyu and Mei, Lingjie and Wang, Mingzhe and Zuo, Yiming and Kayan, Karhan and Wen, Hongyu and Han, Beining and Wang, Yihan and Newell, Alejandro and Law, Hei and Goyal, Ankit and Yang, Kaiyu and Deng, Jia , year =. Infinite. arxiv...
[270]

Learning

Rajeswaran, Aravind and Kumar, Vikash and Gupta, Abhishek and Vezzani, Giulia and Schulman, John and Todorov, Emanuel and Levine, Sergey , year =. Learning. doi:10.48550/arXiv.1709.10087 , urldate =. arxiv , keywords =:1709.10087 , primaryclass =

doi arXiv
[271]

2022 , month = mar, number =

Ramesh, Poornima and Lueckmann, Jan-Matthis and Boelts, Jan and. 2022 , month = mar, number =. doi:10.48550/arXiv.2203.06481 , urldate =. arxiv , keywords =:2203.06481 , primaryclass =

2022 doi
[272]

Hierarchical

Ramesh, Aditya and Dhariwal, Prafulla and Nichol, Alex and Chu, Casey and Chen, Mark , year =. Hierarchical. doi:10.48550/arXiv.2204.06125 , urldate =. arxiv , keywords =:2204.06125 , primaryclass =

doi arXiv
[273]

Generating

Razavi, Ali and van den Oord, Aaron and Vinyals, Oriol , year =. Generating. doi:10.48550/arXiv.1906.00446 , urldate =. arxiv , keywords =:1906.00446 , primaryclass =

1906 doi arXiv
[274]

Reed, Scott and Zolna, Konrad and Parisotto, Emilio and Colmenarejo, Sergio Gomez and Novikov, Alexander and. A. 2022 , month = may, number =. doi:10.48550/arXiv.2205.06175 , urldate =. arxiv , keywords =:2205.06175 , primaryclass =

2022 doi arXiv
[275]

doi:10.48550/arXiv.2204.12581 , urldate =

Rigter, Marc and Lacerda, Bruno and Hawes, Nick , year =. doi:10.48550/arXiv.2204.12581 , urldate =. arxiv , keywords =:2204.12581 , primaryclass =

doi
[276]

Transformer-Based

Robine, Jan and H. Transformer-Based. 2023 , month = mar, number =. doi:10.48550/arXiv.2303.07109 , urldate =. arxiv , keywords =:2303.07109 , primaryclass =

2023 doi
[277]

Rogozhnikov, Alex , year =. Einops:. International
[278]

Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj. High-. 2022 , month = apr, number =. doi:10.48550/arXiv.2112.10752 , urldate =. arxiv , keywords =:2112.10752 , primaryclass =

2022 doi arXiv
[279]

2019 , month = feb, number =

Learning What You Can Do before Doing Anything , author =. 2019 , month = feb, number =. doi:10.48550/arXiv.1806.09655 , urldate =. arxiv , keywords =:1806.09655 , primaryclass =

2019 doi arXiv
[280]

and Gopalakrishnan, Keerthana and Kahatapitiya, Kumara and Xiao, Ted and Rao, Kanishka and Stone, Austin and Lu, Yao and Ibarz, Julian and Arnab, Anurag , year =

Ryoo, Michael S. and Gopalakrishnan, Keerthana and Kahatapitiya, Kumara and Xiao, Ted and Rao, Kanishka and Stone, Austin and Lu, Yao and Ibarz, Julian and Arnab, Anurag , year =. Token. doi:10.48550/arXiv.2211.09119 , urldate =. arxiv , keywords =:2211.09119 , primaryclass =

doi
[281]

2021 , month = nov, number =

Samvelyan, Mikayel and Kirk, Robert and Kurin, Vitaly and. 2021 , month = nov, number =. doi:10.48550/arXiv.2109.13202 , urldate =. arxiv , keywords =:2109.13202 , primaryclass =

2021 doi
[282]

2023 , month = mar, number =

Samvelyan, Mikayel and Khan, Akbir and Dennis, Michael and Jiang, Minqi and. 2023 , month = mar, number =. doi:10.48550/arXiv.2303.03376 , urldate =. arxiv , keywords =:2303.03376 , primaryclass =

2023 doi
[283]

2022 , month = dec, number =

Cooperative Control of Environmental Extremes by Artificial Intelligent Agents , author =. 2022 , month = dec, number =. doi:10.48550/arXiv.2212.02395 , urldate =. arxiv , keywords =:2212.02395 , primaryclass =

2022 doi
[284]

doi:10.48550/arXiv.2301.09515 , urldate =

Sauer, Axel and Karras, Tero and Laine, Samuli and Geiger, Andreas and Aila, Timo , year =. doi:10.48550/arXiv.2301.09515 , urldate =. arxiv , keywords =:2301.09515 , primaryclass =

doi
[285]

Habitat:

Savva, Manolis and Kadian, Abhishek and Maksymets, Oleksandr and Zhao, Yili and Wijmans, Erik and Jain, Bhavana and Straub, Julian and Liu, Jia and Koltun, Vladlen and Malik, Jitendra and Parikh, Devi and Batra, Dhruv , year =. Habitat:. doi:10.48550/arXiv.1904.01201 , urldate...

1904 doi
[286]

Schaeffer, Rylan and Miranda, Brando and Koyejo, Sanmi , year =. Are. doi:10.48550/arXiv.2304.15004 , urldate =. arxiv , keywords =:2304.15004 , primaryclass =

doi
[287]

Prioritized

Schaul, Tom and Quan, John and Antonoglou, Ioannis and Silver, David , year =. Prioritized. arxiv , keywords =:1511.05952 , primaryclass =

arXiv
[288]

Schaul, Tom and Borsa, Diana and Modayil, Joseph and Pascanu, Razvan , year =. Ray. doi:10.48550/arXiv.1904.11455 , urldate =. arxiv , keywords =:1904.11455 , primaryclass =

1904 doi arXiv
[289]

Schlag, Imanol and Sukhbaatar, Sainbayar and Celikyilmaz, Asli and Yih, Wen-tau and Weston, Jason and Schmidhuber, J. Large. 2023 , month = may, number =. doi:10.48550/arXiv.2305.05364 , urldate =. arxiv , keywords =:2305.05364 , primaryclass =

2023 doi
[290]

, editor =

Schmidhuber, J. , editor =. A `. 1993 , pages =. doi:10.1007/978-1-4471-2063-6_107 , abstract =

1993 doi
[291]

Schmidhuber, J. G. arXiv preprint cs/0309048 , year=

arXiv
[292]

Online and

Schrittwieser, Julian and Hubert, Thomas and Mandhane, Amol and Barekatain, Mohammadamin and Antonoglou, Ioannis and Silver, David , year =. Online and. doi:10.48550/arXiv.2104.06294 , urldate =. arxiv , keywords =:2104.06294 , primaryclass =

doi
[293]

and Rodriguez, Adelein and Campbell, Adam and Stanley, Kenneth O

Secretan, Jimmy and Beato, Nicholas and D Ambrosio, David B. and Rodriguez, Adelein and Campbell, Adam and Stanley, Kenneth O. , year =. Picbreeder: Evolving Pictures Collaboratively Online , shorttitle =. Proceedings of the. doi:10.1145/1357054.1357328 , urldate =

doi
[294]

Seo, Younggyo and Hafner, Danijar and Liu, Hao and Liu, Fangchen and James, Stephen and Lee, Kimin and Abbeel, Pieter , year =. Masked. doi:10.48550/arXiv.2206.14244 , urldate =. arxiv , keywords =:2206.14244 , primaryclass =

doi
[295]

Reflexion: An Autonomous Agent with Dynamic Memory and Self-Reflection , shorttitle =

Shinn, Noah and Labash, Beck and Gopinath, Ashwin , year =. Reflexion: An Autonomous Agent with Dynamic Memory and Self-Reflection , shorttitle =. doi:10.48550/arXiv.2303.11366 , urldate =. arxiv , keywords =:2303.11366 , primaryclass =

doi arXiv
[296]

CoRR , volume =

Kashun Shum and Shizhe Diao and Tong Zhang , title =. CoRR , volume =. 2023 , url =. doi:10.48550/arXiv.2302.12822 , eprinttype =. 2302.12822 , timestamp =

2023 doi
[297]

Singer, Uriel and Sheynin, Shelly and Polyak, Adam and Ashual, Oron and Makarov, Iurii and Kokkinos, Filippos and Goyal, Naman and Vedaldi, Andrea and Parikh, Devi and Johnson, Justin and Taigman, Yaniv , year =. Text-. doi:10.48550/arXiv.2301.11280 , urldate =. arxiv , keywor...

doi
[298]

Smith, Jimmy T. H. and Warrington, Andrew and Linderman, Scott W. , year =. Simplified. doi:10.48550/arXiv.2208.04933 , urldate =. arxiv , keywords =:2208.04933 , primaryclass =

doi
[299]

Francis and Abdolmaleki, Abbas and Springenberg, Jost Tobias and Clark, Aidan and Soyer, Hubert and Rae, Jack W

Song, H. Francis and Abdolmaleki, Abbas and Springenberg, Jost Tobias and Clark, Aidan and Soyer, Hubert and Rae, Jack W. and Noury, Seb and Ahuja, Arun and Liu, Siqi and Tirumala, Dhruva and Heess, Nicolas and Belov, Dan and Riedmiller, Martin and Botvinick, Matthew M. , year...

1909 doi
[300]

, year =

Sorscher, Ben and Geirhos, Robert and Shekhar, Shashank and Ganguli, Surya and Morcos, Ari S. , year =. Beyond Neural Scaling Laws: Beating Power Law Scaling via Data Pruning , shorttitle =. doi:10.48550/arXiv.2206.14486 , urldate =. arxiv , keywords =:2206.14486 , primaryclass =

doi

Pith tools

Reviewed May 16, 2026 · model on record in the stance chip above.

[1] [1]

Show Your Work: Scratchpads for Intermediate Computation with Language Models

Maxwell I. Nye and Anders Johan Andreassen and Guy Gur. Show Your Work: Scratchpads for Intermediate Computation with Language Models , journal =. 2021 , url =. 2112.00114 , timestamp =

work page Pith review arXiv 2021

[2] [2]

1995 , publisher=

The Hitchhiker's Guide to the Galaxy , author=. 1995 , publisher=

work page 1995

[3] [3]

NeurIPS , year =

Takeshi Kojima and Shixiang Shane Gu and Machel Reid and Yutaka Matsuo and Yusuke Iwasawa , title =. NeurIPS , year =

work page

[4] [5]

The Eleventh International Conference on Learning Representations,

Yongchao Zhou and Andrei Ioan Muresanu and Ziwen Han and Keiran Paster and Silviu Pitis and Harris Chan and Jimmy Ba , title =. The Eleventh International Conference on Learning Representations,. 2023 , url =

work page 2023

[5] [6]

gradient descent

Automatic prompt optimization with" gradient descent" and beam search , author=. arXiv preprint arXiv:2305.03495 , year=

work page arXiv

[6] [7]

Progressive-Hint Prompting Improves Reasoning in Large Language Models

Progressive-hint prompting improves reasoning in large language models , author=. arXiv preprint arXiv:2304.09797 , year=

work page Pith review arXiv

[7] [8]

Artificial Intelligence , volume=

Reward is enough , author=. Artificial Intelligence , volume=. 2021 , publisher=

work page 2021

[8] [9]

Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models , author=. arXiv preprint arXiv:2305.04091 , year=

work page Pith review arXiv