How Should We Meta-Learn Reinforcement Learning Algorithms? | Paper Citation Record

Observation a3d7ccb0-92bc-4646-b308-e5a76539886e · outbound

This paper cites Loss of Plasticity in Continual Deep Reinforcement Learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Loss of Plasticity in Continual Deep Reinforcement Learning

Reference 1

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Observation 24da469e-b54b-4146-bb26-ec474352ed58 · outbound

This paper cites Towards Characterizing Divergence in Deep Q-Learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Towards Characterizing Divergence in Deep Q-Learning

Reference 2

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Observation f48faf5f-8f10-4095-b923-e3fe5bf3f8f4 · outbound

This paper cites A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning

Reference 3

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Observation b19f019a-93e9-4b0d-b221-c531836e6370 · outbound

This paper cites Deep reinforcement learning at the edge of the statistical precipice.

How Should We Meta-Learn Reinforcement Learning Algorithms? Deep reinforcement learning at the edge of the statistical precipice

Reference 4

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Observation 44b1374f-aa2b-4bff-a13f-84907f107a90 · outbound

This paper cites A Generalizable Approach to Learning Optimizers.

How Should We Meta-Learn Reinforcement Learning Algorithms? A Generalizable Approach to Learning Optimizers

Reference 5

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Observation 34b95b01-ac5e-4eaf-afc5-9dd2e849b3f3 · outbound

This paper cites Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando de Freitas.

How Should We Meta-Learn Reinforcement Learning Algorithms? Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando de Freitas

Reference 6

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Observation 2fafa30b-8079-487a-bd81-65d3d1db55d3 · outbound

This paper cites An information-theoretic perspective on intrinsic motivation in reinforcement learning: A survey.

How Should We Meta-Learn Reinforcement Learning Algorithms? An information-theoretic perspective on intrinsic motivation in reinforcement learning: A survey

Reference 7

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Observation 59369bea-951d-4879-922e-ac071fdf0ea3 · outbound

This paper cites A Tutorial on Meta-Reinforcement Learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? A Tutorial on Meta-Reinforcement Learning

Reference 8

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Observation 8fd95d5d-5e2d-46b6-84db-12a0aa2f778f · outbound

This paper cites OpenAI gym, 2016.

How Should We Meta-Learn Reinforcement Learning Algorithms? OpenAI gym, 2016

Reference 9

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Observation 379f9e2e-23a4-4b86-8f11-d59bb3c045d6 · outbound

This paper cites Exploration by Random Network Distillation.

How Should We Meta-Learn Reinforcement Learning Algorithms? Exploration by Random Network Distillation

Reference 10

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Observation 5dc19526-a0f3-4c51-bbe0-c15aaf14490d · outbound

This paper cites Boltzmann exploration done right.

How Should We Meta-Learn Reinforcement Learning Algorithms? Boltzmann exploration done right

Reference 11

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Observation 2049f298-757a-4752-a563-7d78d809c499 · outbound

This paper cites Symbolic Discovery of Optimization Algorithms.

How Should We Meta-Learn Reinforcement Learning Algorithms? Symbolic Discovery of Optimization Algorithms

Reference 12

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Observation 1e634b23-c977-4fb1-adee-0abef0b42e18 · outbound

This paper cites Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl.

How Should We Meta-Learn Reinforcement Learning Algorithms? Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl

Reference 13

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Observation 1aea3f56-9912-422e-8722-f2befb70c15f · outbound

This paper cites Discovering Symbolic Models from Deep Learning with Inductive Biases.

How Should We Meta-Learn Reinforcement Learning Algorithms? Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 14

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Observation 707eb909-41c4-4c9a-bf29-55fb711e05c8 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 15

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Observation 96506966-4351-4c0c-80b6-4511d5ffe9e5 · outbound

This paper cites Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design.

How Should We Meta-Learn Reinforcement Learning Algorithms? Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

Reference 16

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Observation 82b12f95-c64e-4c2d-849b-84e96f4da4d9 · outbound

This paper cites Loss of plasticity in deep continual learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Loss of plasticity in deep continual learning

Reference 17

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Observation bad298e5-b312-469a-9370-21d35555a317 · outbound

This paper cites RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Reference 18

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Observation f70732c3-e78c-447d-b9f7-793c88ad0058 · outbound

This paper cites Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps.

How Should We Meta-Learn Reinforcement Learning Algorithms? Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps

Reference 19

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Observation e97cd32c-805c-4709-bced-cce321896e67 · outbound

This paper cites OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code.

How Should We Meta-Learn Reinforcement Learning Algorithms? OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code

Reference 20

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Observation 6454b23b-eafe-4b95-bb70-59f7f5a77502 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

How Should We Meta-Learn Reinforcement Learning Algorithms? Model-agnostic meta-learning for fast adaptation of deep networks

Reference 21

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Observation 23895565-0168-4277-9fd2-a917f64ddf2e · outbound

This paper cites Noisy Networks for Exploration.

How Should We Meta-Learn Reinforcement Learning Algorithms? Noisy Networks for Exploration

Reference 22

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Observation faaafd20-4489-409b-b34e-4e5bd93cbcb1 · outbound

This paper cites Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, and Olivier Bachem.

How Should We Meta-Learn Reinforcement Learning Algorithms? Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, and Olivier Bachem

Reference 23

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Observation 0648e23b-f653-4be6-b3c1-e4613c3559fc · outbound

This paper cites Born Again Neural Networks.

How Should We Meta-Learn Reinforcement Learning Algorithms? Born Again Neural Networks

Reference 24

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Observation c4e7164c-4f0c-4ab6-a9c1-9520d6a6f49a · outbound

This paper cites Goldie, Chris Lu, Matthew T.

How Should We Meta-Learn Reinforcement Learning Algorithms? Goldie, Chris Lu, Matthew T

Reference 25

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Observation 0d14a9d1-f1ad-4f76-9b59-997042574c7c · outbound

This paper cites Benchmarking the Spectrum of Agent Capabilities.

How Should We Meta-Learn Reinforcement Learning Algorithms? Benchmarking the Spectrum of Agent Capabilities

Reference 26

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This paper cites Distilling the Knowledge in a Neural Network.

How Should We Meta-Learn Reinforcement Learning Algorithms? Distilling the Knowledge in a Neural Network

Reference 27

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Observation 4080903f-56ca-481e-9526-3d647585a01f · outbound

This paper cites Long short-term memory.

How Should We Meta-Learn Reinforcement Learning Algorithms? Long short-term memory

Reference 28

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This paper cites Automated Design of Agentic Systems.

How Should We Meta-Learn Reinforcement Learning Algorithms? Automated Design of Agentic Systems

Reference 29

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This paper cites Transient non-stationarity and generalisation in deep reinforcement learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Transient non-stationarity and generalisation in deep reinforcement learning

Reference 30

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Observation 0edfac8e-8273-47a5-aa23-a12663df7d34 · outbound

This paper cites Transient non-stationarity and generalisation in deep reinforcement learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Transient non-stationarity and generalisation in deep reinforcement learning

Reference 31

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Observation 7d42ccf3-7005-4fd5-8841-a55667aa143c · outbound

This paper cites Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design.

How Should We Meta-Learn Reinforcement Learning Algorithms? Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design

Reference 32

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This paper cites Discovering temporally-aware reinforcement learning algorithms.

How Should We Meta-Learn Reinforcement Learning Algorithms? Discovering temporally-aware reinforcement learning algorithms

Reference 33

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This paper cites Improving policy optimization with generalist-specialist learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Improving policy optimization with generalist-specialist learning

Reference 34

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This paper cites Meta Learning Backpropagation And Improving It.

How Should We Meta-Learn Reinforcement Learning Algorithms? Meta Learning Backpropagation And Improving It

Reference 35

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This paper cites Improving generalization in meta reinforcement learning using learned objectives.

How Should We Meta-Learn Reinforcement Learning Algorithms? Improving generalization in meta reinforcement learning using learned objectives

Reference 36

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This paper cites Mirror Learning: A Unifying Framework of Policy Optimisation.

How Should We Meta-Learn Reinforcement Learning Algorithms? Mirror Learning: A Unifying Framework of Policy Optimisation

Reference 37

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This paper cites Learning to Optimize for Reinforcement Learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Learning to Optimize for Reinforcement Learning

Reference 38

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This paper cites gymnax: A JAX -based reinforcement learning environment library, 2022 a.

How Should We Meta-Learn Reinforcement Learning Algorithms? gymnax: A JAX -based reinforcement learning environment library, 2022 a

Reference 39

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This paper cites evosax: JAX-based Evolution Strategies.

How Should We Meta-Learn Reinforcement Learning Algorithms? evosax: JAX-based Evolution Strategies

Reference 40

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This paper cites In-context reinforcement learning with algorithm distillation.

How Should We Meta-Learn Reinforcement Learning Algorithms? In-context reinforcement learning with algorithm distillation

Reference 41

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How Should We Meta-Learn Reinforcement Learning Algorithms? Evolution through Large Models

Reference 42

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How Should We Meta-Learn Reinforcement Learning Algorithms? Rediscovering orbital mechanics with machine learning

Reference 43

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This paper cites Discovered policy optimisation.

How Should We Meta-Learn Reinforcement Learning Algorithms? Discovered policy optimisation

Reference 44

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This paper cites Discovering Preference Optimization Algorithms with and for Large Language Models.

How Should We Meta-Learn Reinforcement Learning Algorithms? Discovering Preference Optimization Algorithms with and for Large Language Models

Reference 45

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This paper cites Behaviour Distillation.

How Should We Meta-Learn Reinforcement Learning Algorithms? Behaviour Distillation

Reference 46

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This paper cites Understanding plasticity in neural networks.

How Should We Meta-Learn Reinforcement Learning Algorithms? Understanding plasticity in neural networks

Reference 47

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This paper cites Craftax: a lightning-fast benchmark for open-ended reinforcement learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Craftax: a lightning-fast benchmark for open-ended reinforcement learning

Reference 48

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This paper cites Interpretable machine learning methods applied to jet background subtraction in heavy-ion collisions.

How Should We Meta-Learn Reinforcement Learning Algorithms? Interpretable machine learning methods applied to jet background subtraction in heavy-ion collisions

Reference 49

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This paper cites Meta-Learning Update Rules for Unsupervised Representation Learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Meta-Learning Update Rules for Unsupervised Representation Learning

Reference 50

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How Should We Meta-Learn Reinforcement Learning Algorithms? Understanding and correcting pathologies in the training of learned optimizers

Reference 51

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This paper cites Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves.

How Should We Meta-Learn Reinforcement Learning Algorithms? Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves

Reference 52

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This paper cites Gradients are Not All You Need.

How Should We Meta-Learn Reinforcement Learning Algorithms? Gradients are Not All You Need

Reference 53

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This paper cites VeLO: Training Versatile Learned Optimizers by Scaling Up.

How Should We Meta-Learn Reinforcement Learning Algorithms? VeLO: Training Versatile Learned Optimizers by Scaling Up

Reference 54

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This paper cites Self-distillation amplifies regularization in hilbert space.

How Should We Meta-Learn Reinforcement Learning Algorithms? Self-distillation amplifies regularization in hilbert space

Reference 55

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This paper cites Small batch deep reinforcement learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Small batch deep reinforcement learning

Reference 56

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This paper cites Discovering reinforcement learning algorithms.

How Should We Meta-Learn Reinforcement Learning Algorithms? Discovering reinforcement learning algorithms

Reference 57

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This paper cites Openai o3-mini, January 2025.

How Should We Meta-Learn Reinforcement Learning Algorithms? Openai o3-mini, January 2025

Reference 58

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This paper cites Stabilizing transformers for reinforcement learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Stabilizing transformers for reinforcement learning

Reference 59

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Observation b3da679a-906b-4c9d-810a-8580885f52c3 · outbound

This paper cites Evolving Curricula with Regret - Based Environment Design.

How Should We Meta-Learn Reinforcement Learning Algorithms? Evolving Curricula with Regret - Based Environment Design

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:48:55.589660Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8f69eaaf-88c3-44ef-a1dd-9c73bda00a80 · outbound

This paper cites Parameter Space Noise for Exploration.

How Should We Meta-Learn Reinforcement Learning Algorithms? Parameter Space Noise for Exploration

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:50.593230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d359abff-4acd-473c-8099-de350efa7649 · outbound

This paper cites Tunability: Importance of hyperparameters of machine learning algorithms.

How Should We Meta-Learn Reinforcement Learning Algorithms? Tunability: Importance of hyperparameters of machine learning algorithms

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:48:55.346301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a2e2d967-75c0-47e9-8a37-fb87210c6462 · outbound

This paper cites Evolutionsstrategie : Optimierung technischer systeme nach prinzipien der biologischen evolution.

How Should We Meta-Learn Reinforcement Learning Algorithms? Evolutionsstrategie : Optimierung technischer systeme nach prinzipien der biologischen evolution

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:48:55.153556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9bb36e11-6709-4c14-9b4c-ea544ef8c666 · outbound

This paper cites Pawan Kumar, Emilien Dupont, Francisco J.

How Should We Meta-Learn Reinforcement Learning Algorithms? Pawan Kumar, Emilien Dupont, Francisco J

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:50.860051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f77d5312-72e7-4aeb-93d5-b885a82980a1 · outbound

This paper cites Policy Distillation.

How Should We Meta-Learn Reinforcement Learning Algorithms? Policy Distillation

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:50.969246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dfd04f3-40b7-4c24-ac86-7f19ddad9f85 · outbound

This paper cites Evolution Strategies as a Scalable Alternative to Reinforcement Learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:51.080292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21e3588a-9d4c-44ad-b16e-341ea7d14213 · outbound

This paper cites Proximal Policy Optimization Algorithms.

How Should We Meta-Learn Reinforcement Learning Algorithms? Proximal Policy Optimization Algorithms

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:51.130615Z

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Observation 30db874b-70dd-4448-bbc1-1cb7d6c5b9ef · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

How Should We Meta-Learn Reinforcement Learning Algorithms? High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:51.229277Z

Source-reported events for the cited work

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Observation 945e19ab-25ed-45ef-90d3-58f813481d85 · outbound

This paper cites The Dormant Neuron Phenomenon in Deep Reinforcement Learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? The Dormant Neuron Phenomenon in Deep Reinforcement Learning

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:51.326750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d765ea3d-991b-4127-946d-725e108653ad · outbound

This paper cites Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:51.431138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f177879-b4e0-4a29-adab-9a732bd859f2 · outbound

This paper cites Generalizable Symbolic Optimizer Learning.

How Should We Meta-Learn Reinforcement Learning Algorithms? Generalizable Symbolic Optimizer Learning

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:48:55.021535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cf324fbd-f556-48f8-ba71-a16468de3b0e · outbound

This paper cites Position: Leverage Foundational Models for Black-Box Optimization.

How Should We Meta-Learn Reinforcement Learning Algorithms? Position: Leverage Foundational Models for Black-Box Optimization

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:51.550816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d92d76f-80a5-4dc5-9d43-0693b7e3567e · outbound

This paper cites Maxinfo RL : Boosting exploration in reinforcement learning through information gain maximization.

How Should We Meta-Learn Reinforcement Learning Algorithms? Maxinfo RL : Boosting exploration in reinforcement learning through information gain maximization

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:48:54.840199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9f2931ef-91ed-4c19-9777-88dfd6d5e8e6 · outbound

This paper cites Sutton and Andrew Barto.

How Should We Meta-Learn Reinforcement Learning Algorithms? Sutton and Andrew Barto

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:48:54.668222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3acf0046-0b42-41dd-8def-cac57e2a55b3 · outbound

This paper cites Improving deep reinforcement learning by reducing the chain effect of value and policy churn.

How Should We Meta-Learn Reinforcement Learning Algorithms? Improving deep reinforcement learning by reducing the chain effect of value and policy churn

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:51.798344Z

Source-reported events for the cited work

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Observation 7658bc7e-8099-4ad4-8afb-4a365f10451b · outbound

This paper cites MuJoCo : A physics engine for model-based control.

How Should We Meta-Learn Reinforcement Learning Algorithms? MuJoCo : A physics engine for model-based control

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:51.925909Z

Source-reported events for the cited work

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Observation 9d4d48f1-3bb7-4255-a172-039e321357a0 · outbound

This paper cites Deep Reinforcement Learning and the Deadly Triad.

How Should We Meta-Learn Reinforcement Learning Algorithms? Deep Reinforcement Learning and the Deadly Triad

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:52.022023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9178950-d006-48d7-84c0-46e344cce11d · outbound

This paper cites Attention Is All You Need.

How Should We Meta-Learn Reinforcement Learning Algorithms? Attention Is All You Need

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:52.137791Z

Source-reported events for the cited work

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Observation 8f07cb6a-ce7b-45e4-8917-29771434a0ae · outbound

This paper cites Dataset Distillation.

How Should We Meta-Learn Reinforcement Learning Algorithms? Dataset Distillation

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:52.204307Z

Source-reported events for the cited work

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Observation 59339402-f08a-4723-bd2c-7434f8185b54 · outbound

This paper cites Natural Evolution Strategies.

How Should We Meta-Learn Reinforcement Learning Algorithms? Natural Evolution Strategies

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:48:53.127951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 985ed171-fb21-495a-b2b0-84d329f3aa4b · outbound

This paper cites Understanding short-horizon bias in stochastic meta-optimization.

How Should We Meta-Learn Reinforcement Learning Algorithms? Understanding short-horizon bias in stochastic meta-optimization

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:48:54.523597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1e7ab7c4-6b5d-4d2a-8ff5-7a5056aac1b8 · outbound

This paper cites MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments.

How Should We Meta-Learn Reinforcement Learning Algorithms? MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:52.488734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc47c1d0-9afe-4395-bec3-813cad431c96 · outbound

This paper cites Self-distillation as instance-specific label smoothing.

How Should We Meta-Learn Reinforcement Learning Algorithms? Self-distillation as instance-specific label smoothing

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:48:54.343900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 44945f70-98c5-4abf-b02c-65bc7fd38f80 · outbound

This paper cites Symbolic Learning to Optimize: Towards Interpretability and Scalability.

How Should We Meta-Learn Reinforcement Learning Algorithms? Symbolic Learning to Optimize: Towards Interpretability and Scalability

Reference 84

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:48:52.957731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 41439ad1-985a-4ebb-b5bf-0348a9646f81 · outbound

This paper cites write newline.

How Should We Meta-Learn Reinforcement Learning Algorithms? write newline

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:52.758471Z

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