Pith. sign in

REVIEW 3 major objections 4 cited by

LLM-as-a-Verifier: A General-Purpose Verification Framework

T0 review · 3 major / 0 minor · reviewed 2026-07-11 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Verification is a new scaling axis for language models: continuous scores from scoring-token logits, scaled on three knobs, select better agent trajectories without training a judge.

desk verdict Solid multi-domain systems paper: continuous score-token expectation plus G/K/C scaling and PPT ranking beat discrete judges and several trained robotics RMs, with real ablations—but novelty is packaging more than a new principle. read the letter →

arxiv 2607.05391 v2 pith:H375NX5S submitted 2026-07-06 cs.AI cs.CLcs.LGcs.MAcs.RO

Jacky Kwok , Shulu Li , Pranav Atreya , Yuejiang Liu , Yixing Jiang , Chelsea Finn , Marco Pavone , Ion Stoica
show 1 more author
Azalia Mirhoseini
This is my paper · ORCID
classification cs.AIcs.CLcs.LGcs.MAcs.RO
keywords LLM-as-a-Verifierverificationscalingscoring-tokenlogitstrajectoryrewardmodeltest-timedenserewardsagentictasksBradley-Terryranking
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

The paper argues that deciding whether a solution is correct has been left behind while generation has scaled, and that verification itself can be scaled. Standard language-model judges collapse their scoring distribution into a single discrete token, which creates ties and weak discrimination among long agent trajectories. LLM-as-a-Verifier instead takes the expectation over the full distribution of scoring-token logits, producing continuous scores. That probabilistic formulation opens three practical knobs: finer score granularity, repeated evaluations, and decomposition into simpler criteria. With a budget-aware ranking procedure that concentrates comparisons on the most promising candidates, the same training-free verifier reaches new highs on coding, robotics, and medical agent benchmarks and supplies dense progress signals that speed up both off-policy and on-policy reinforcement learning.

What carries the argument

The continuous reward of Equation 3.1: the average, over criteria and repeated evaluations, of the expected scalar value of the scoring-token distribution. Pairwise preferences follow from a Bradley–Terry model on those rewards; a Probabilistic Pivot Tournament then ranks N candidates in O(Nk) comparisons by first breaking positional bias with a random ring pass and then concentrating remaining budget on a small set of pivots.

What would settle it

On a held-out set of trajectory pairs whose ground-truth correctness is known, replace continuous logit expectations with ordinary discrete argmax scores (or with random continuous scores of matched variance) while keeping the same ranking procedure; if the accuracy and progress-correlation gains disappear, the central claim fails.

Watch

Extended reading notes

Core claim

Verification quality improves systematically when continuous rewards are formed as the expectation over scoring-token logits and then scaled along score granularity, number of repeated evaluations, and criteria decomposition. Combined with a cost-efficient pivot tournament that converts those continuous scores into preference probabilities, the framework selects better trajectories at test time and supplies denser rewards for reinforcement learning, all without training a specialized reward model.

Load-bearing premise

That a language model’s probability mass over scoring tokens, when prompted with hand-written criteria, is a calibrated, domain-general measure of trajectory quality rather than an artifact of prompt style or superficial cues.

Editorial extensions

If this is right

  • Best-of-N selection for long-horizon coding, robotics, and medical agents can improve without any new reward-model training.
  • Live progress scores become available for monitoring and early abort of agentic systems.
  • Dense verifier rewards raise sample efficiency of both SAC-style and GRPO-style reinforcement learning.
  • Verification compute can be dialed independently of generation compute via the three scaling knobs and the pivot budget.
  • The same recipe transfers across text and multi-frame video once log-probabilities (or a two-stage open-model handoff) are available.

Reading between the lines

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

  • If scoring-token mass is truly calibrated, criteria decomposition may eventually be generated automatically per domain rather than hand-designed, turning the third knob into a learned object.
  • The same continuous signal could serve as a runtime monitor that freezes or rolls back an agent when the progress curve plateaus or declines, independent of final-outcome ranking.
  • Closed models that withhold logits may still participate via the two-stage handoff, which suggests a practical division of labor: frontier reasoning + open calibrated scoring.
  • Because the framework is training-free, gains should appear most clearly on domains where collecting preference data for a learned reward model is expensive or safety-critical.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 0 minor

Summary. The paper proposes LLM-as-a-Verifier, a training-free verification framework that scores agent trajectories by taking the expectation of a scalar map over the model’s scoring-token probability mass (Eq. 3.1), rather than collapsing to a discrete argmax score as in standard LM judges. It argues that this continuous formulation unlocks three complementary verification-scaling axes—score granularity G, repeated evaluation K, and criteria decomposition C—and introduces Probabilistic Pivot Tournament (PPT) to select among N candidates at O(Nk) pairwise cost. Empirically, the method reports improved pairwise verification accuracy under controlled G/K/C budgets (Fig. 4, Table 1, Fig. 7), competitive or state-of-the-art trajectory selection on Terminal-Bench V2 (86.5%), SWE-Bench Verified (78.2%), RoboRewardBench (87.4% preference accuracy), and MedAgentBench (73.3%), Value-Order Correlation as a progress proxy, and denser rewards that improve sample efficiency of DSRL-SAC on LIBERO and GRPO on MATH (Fig. 9).

Significance. If the results hold under broader scrutiny, the paper makes a useful contribution to test-time scaling and agent evaluation: it reframes verification quality as something that can be improved by compute and prompt structure without reward-model training, and it demonstrates transfer across coding, robotics video, and medical agent settings with a single probabilistic recipe. Strengths include controlled scaling plots, an explicit SNR decomposition for granularity (Table 1), a concrete case study with tie-rate analysis (Table 2), a budget–accuracy characterization of PPT (Table 9), multi-benchmark selection results with Pass@1/oracle context (Table 3), and dual use of the same signal for progress monitoring and RL shaping. The practical artifacts (Claude Code/Codex-style extension, harness generalization in Appendix B.1, closed-model two-stage workaround in B.6) increase the work’s utility beyond a pure leaderboard claim.

major comments (3)
  1. §3.2 Eq. (3.1)–(3.2) and §4.1: The central interpretation—that continuous scoring-token expectations yield more calibrated correctness comparisons—is supported by SNR growth, lower tie rates, and accuracy gains, but the manuscript does not adequately rule out surface-form confounds (trajectory length, log verbosity, tool-call polish, formatting). The query-optimize case (Table 2, App. B.4) shows the model can identify a real methodological failure while still expressing it in hedged language, which is consistent with either calibrated belief or stylistic confidence. A load-bearing addition would be matched-surface ablations or partial correlations of R(x,τ) with length/verbosity after conditioning on success/failure; without this, the claim that verification is a distinct scaling axis (vs. a better readout of existing judge biases) remains incompletely stress-tested.
  2. §4.3 and the multi-domain experiments in §5: Criteria decomposition is presented as a general scaling axis, yet the concrete factors (Specification/Output/Errors for code; domain-specific criteria elsewhere) are hand-designed. Fig. 4 (right) shows ensemble gains, but there is no sensitivity analysis to criterion wording, number, or quality, nor a clear protocol for constructing criteria in a new domain. Because SOTA numbers use G=20, K=8, and the three-criterion setup, the paper should quantify how much of the reported selection accuracy is attributable to rubric engineering versus the continuous formulation and PPT; otherwise the “general-purpose / no additional training” claim overstates plug-and-play generality.
  3. §5 and Table 3: Absolute “state-of-the-art” framing should be tightened. The fairest evidence is improvement over Pass@1 on the same candidate pools (e.g., Terminal-Bench 83.1%→86.5%, SWE-Bench 76.1%→78.2%, MedAgent 70.2%→73.3%), which is real but modest relative to oracle headroom. Leaderboard comparisons mix harnesses, proposal models, and N; SWE-Bench further uses a heterogeneous three-model pool, so selection partly includes cross-model routing. Please report selection accuracy with uncertainty (bootstrap/task-level variance), fix N and harness when comparing to named baselines, and separate “best-of-N with our verifier” from “beats published single-trajectory leaderboard entries.”

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: continuous scores are a defined estimator evaluated against external ground truth, not predictions forced by construction.

full rationale

LLM-as-a-Verifier is an empirical methods paper. Equation 3.1 defines the continuous reward as the expectation of scoring-token values under the prompted model’s logits, averaged over criteria C and repeats K; Equations 3.2 and the Probabilistic Pivot Tournament then convert those scores into pairwise preferences for selection. These are definitions of the proposed estimator, not derivations that smuggle the target into the inputs. Verification accuracy, SOTA trajectory selection (Terminal-Bench V2, SWE-Bench Verified, RoboRewardBench, MedAgentBench), Value-Order Correlation, and RL sample-efficiency gains are all measured against external labels: hidden task graders, human preference annotations, chronological step order, and environment success rates. Hyperparameters G, K, C, k, λ, β are ablated under controlled budgets rather than fitted so as to force the reported accuracies. There is no self-definitional loop (X defined via Y then used to “predict” Y), no fitted parameter renamed as a prediction of the same quantity, no load-bearing uniqueness theorem imported from overlapping authors, and no ansatz smuggled in via self-citation that forbids alternatives. Minor self-citations in related work (e.g., prior robotics verification papers) are not load-bearing for the central claims. The derivation chain is therefore self-contained against external benchmarks; circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 2 invented entities

The central claims rest on standard probabilistic ranking assumptions, the premise that prompted LLM/VLM logits encode useful quality/progress beliefs, hand-chosen evaluation criteria and scaling hyperparameters, and access (direct or staged) to scoring-token probabilities. No new physical entities are postulated; the invented pieces are algorithmic constructs evaluated empirically.

free parameters (5)
  • score granularity G
    Number of scoring tokens (up to 20) chosen as a scaling knob; main results use G=20.
  • repeated evaluations K
    Monte Carlo repeats of verification; main results often use K=8 (robotics VOC uses K=5).
  • criteria count/decomposition C
    Hand-designed sub-criteria (e.g., Specification/Output/Errors for code) averaged in Eq. 3.1.
  • pivot count k in PPT
    Budget–accuracy trade-off parameter reducing comparisons from O(N^2) to O(Nk); ablated in Table 9.
  • RL shaping weights λ and β
    Trade-off coefficients for verifier progress/reasoning rewards in SAC (λ=1) and GRPO (β=0.1).
assumptions (4)
  • standard math Bradley–Terry model maps continuous reward differences to pairwise preference probabilities (Eq. 3.2).
    Standard ranking assumption used to aggregate soft wins in PPT.
  • domain assumption Scoring-token logprobs of a prompted LLM/VLM are a useful, generalizable signal of trajectory correctness/progress without task-specific reward training.
    Load-bearing premise of the entire training-free framework (§3–§7).
  • ad hoc to paper Hand-written domain criteria and pairwise prompts adequately factor long-horizon quality for coding, robotics video, and medical agents.
    Criteria decomposition is presented as a scaling axis but designed by authors (§4.3).
  • ad hoc to paper For logit-restricted APIs, free-form frontier reasoning plus open-model logprob scoring recovers most continuous-verifier benefit.
    Appendix B.6 workaround needed for closed models; not the primary setup.
invented entities (2)
  • LLM-as-a-Verifier continuous reward (Eq. 3.1) independent evidence
    purpose: Define training-free fine-grained trajectory scores from expected scoring-token values averaged over criteria and repeats.
    Methodological construct; evaluated against external outcomes rather than postulated as a physical object.
  • Probabilistic Pivot Tournament (PPT) independent evidence
    purpose: Select best of N candidates under O(Nk) verification budget with ring-pass bias cancellation.
    Algorithmic invention; budget–accuracy characterized on Terminal-Bench candidate pools.

how reviews work

0 comments
Cite this review

Pith. "Pith review of LLM-as-a-Verifier: A General-Purpose Verification Framework." pith.science (2026). https://pith.science/paper/H375NX5S

@misc{pith2026260705391,
  author       = {Pith},
  title        = {Pith review of: LLM-as-a-Verifier: A General-Purpose Verification Framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H375NX5S}},
  note         = {Machine review of arXiv:2607.05391}
}
read the original abstract

Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs. In this work, we identify verification, the ability to determine the correctness of a solution, as a new scaling axis. To unlock this and demonstrate its effectiveness, we introduce LLM-as-a-Verifier, a general-purpose verification framework that provides fine-grained feedback for agentic tasks without requiring additional training. Unlike standard LM judges that prompt LLMs to produce discrete scores for candidate solutions, LLM-as-a-Verifier computes the expectation over the distribution of scoring token logits to generate continuous scores. This probabilistic formulation enables verification to scale along multiple dimensions: (1) score granularity, (2) repeated evaluation, and (3) criteria decomposition. In particular, we show that scaling the scoring granularity leads to better separation between positive and negative solutions, resulting in more calibrated comparisons. Moreover, scaling repeated evaluation and criteria decomposition consistently lead to additional gains in verification accuracy through variance and complexity reduction. We further introduce a cost-efficient ranking algorithm for selecting the best solution among candidates using the verifier's continuous scores. LLM-as-a-Verifier achieves state-of-the-art performance on Terminal-Bench V2 (86.5%), SWE-Bench Verified (78.2%), RoboRewardBench (87.4%), and MedAgentBench (73.3%). Beyond verification, the fine-grained signals from LLM-as-a-Verifier can also serve as a proxy for estimating task progress. We build an extension for Claude Code, enabling developers to monitor and improve their own agentic systems. Finally, we show that LLM-as-a-Verifier can provide dense feedback for RL, improving the sample efficiency of SAC and GRPO on robotics and mathematical reasoning benchmarks.

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SERPO: Self-Evolving Rubric Policy Optimization for Open-Ended Test-Time Reinforcement Learning

    cs.CL 2026-07 conditional novelty 7.0 of 10

    SERPO co-evolves per-question grading rubrics, Good-Normal-Bad response archives, and policy parameters so a language model can train itself at inference time without labels, gaining up to 20.6 points on open-ended me...

  2. ResidencyRL: Reinforcement Learning in Simulated Clinical Environments

    cs.AI 2026-08 conditional novelty 6.0 of 10

    A reinforcement learning method that trains a medical AI through long multi-turn simulated patient encounters improves diagnostic and management quality and is preferred by clinicians over its base model.

  3. When Policies Change Probabilities: Modular Decision-Making for LLM Code Review

    cs.SE 2026-08 conditional novelty 6.0 of 10

    Swapping a 1:1 for a 10:1 false-accept policy shifted reported failure probabilities by 13.6 to 16.9 percentage points across four LLM reviewer systems, and the actions from the high-cost prompt were worse than reject...

  4. Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time Collaboration

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Training-free LLM collaboration gains are bounded by the fixed pool's oracle gap and then by signal coverage, fidelity, and harm, measurable with a small labeled audit.

Reference graph

Works this paper leans on

95 extracted references · 59 linked inside Pith · cited by 4 Pith papers

  1. [1]

    Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei

    Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling Laws for Neural Language Models, January 2020. URLhttp://arxiv.org/abs/2001.08361. arXiv:2001.08361 [cs]

    arXiv 2020

  2. [2]

    Scaling Laws for Reward Model Overoptimization, October 2022

    Leo Gao, John Schulman, and Jacob Hilton. Scaling Laws for Reward Model Overoptimization, October 2022. URLhttp://arxiv.org/abs/2210.10760. arXiv:2210.10760 [cs]

    arXiv 2022

  3. [3]

    Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters, August 2024

    Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters, August 2024. URLhttp://arxiv.org/abs/ 2408.03314. arXiv:2408.03314 [cs]

    arXiv 2024

  4. [4]

    Xing, Hao Zhang, Joseph E

    Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging LLM-as-a-judge with MT-Bench and Chatbot Arena, 2023. URLhttps://arxiv.org/abs/ 2306.05685

    arXiv 2023

  5. [5]

    URLhttps://arxiv.org/abs/2603.04304

    HarmanSingh,XiuyuLi,KushaSareen,MonishwaranMaheswaran,SijunTan,XiaoxiaWu,Junxiong Wang, Alpay Ariyak, Qingyang Wu, Samir Khaki, Rishabh Tiwari, Long Lian, Yucheng Lu, Boyi Li, Alane Suhr, Ben Athiwaratkun, and Kurt Keutzer.𝑉1: Unifying Generation and Self-Verification for Parallel Reasoners, 2026. URLhttps://arxiv.org/abs/2603.04304

    arXiv 2026

  6. [6]

    Generative verifiers: Reward modeling as next-token prediction, 2025

    Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, and Rishabh Agarwal. Generative verifiers: Reward modeling as next-token prediction, 2025. URLhttps://arxiv.org/ abs/2408.15240

    arXiv 2025

  7. [7]

    Training Verifiers to Solve Math Word Problems, November 2021

    KarlCobbe,VineetKosaraju,MohammadBavarian,MarkChen,HeewooJun,LukaszKaiser,Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training Verifiers to Solve Math Word Problems, November 2021. URLhttp://arxiv.org/abs/ 2110.14168. arXiv:2110.14168 [cs]

    arXiv 2021

  8. [8]

    Let’s Verify Step by Step, May 2023

    Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let’s Verify Step by Step, May 2023. URL http://arxiv.org/abs/2305.20050. arXiv:2305.20050 [cs]

    arXiv 2023

Show all 95 references
  1. [9]

    Robometer: Scaling general-purpose robotic reward models via trajectory comparisons.arXiv preprint arXiv:2603.02115, 2026

    Anthony Liang, Yigit Korkmaz, Jiahui Zhang, Minyoung Hwang, Abrar Anwar, Sidhant Kaushik, Aditya Shah, Alex S Huang, Luke Zettlemoyer, Dieter Fox, et al. Robometer: Scaling general-purpose robotic reward models via trajectory comparisons.arXiv preprint arXiv:2603.02115, 2026. ...

    2026 arXiv

  2. [10]

    Topreward: Token probabilities as hidden zero-shot rewards for robotics.arXiv preprint arXiv:2602.19313, 2026

    Shirui Chen, Cole Harrison, Ying-Chun Lee, Angela Jin Yang, Zhongzheng Ren, Lillian J Ratliff, Jiafei Duan, Dieter Fox, and Ranjay Krishna. Topreward: Token probabilities as hidden zero-shot rewards for robotics.arXiv preprint arXiv:2602.19313, 2026

    2026

  3. [11]

    RoboReward: General-purpose vision-language reward models for robotics, 2026

    Tony Lee, Andrew Wagenmaker, Karl Pertsch, Percy Liang, Sergey Levine, and Chelsea Finn. RoboReward: General-purpose vision-language reward models for robotics, 2026. URLhttps: //arxiv.org/abs/2601.00675

    2026

  4. [12]

    Libero: Benchmarking knowledge transfer for lifelong robot learning, 2023

    Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning, 2023. URLhttps://arxiv.org/ abs/2306.03310

    2023 arXiv

  5. [13]

    Steering your diffusion policy with latent space reinforcement learning, 2025

    Andrew Wagenmaker, Mitsuhiko Nakamoto, Yunchu Zhang, Seohong Park, Waleed Yagoub, Anusha Nagabandi, Abhishek Gupta, and Sergey Levine. Steering your diffusion policy with latent space reinforcement learning, 2025. URLhttps://arxiv.org/abs/2506.15799

    2025 arXiv

  6. [14]

    DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei F...

    doi arXiv

  7. [15]

    Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, 18 LLM-as-a-Verifier: A General-Purpose Verification Framework Dario Amodei, and Paul Christiano

    Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, 18 LLM-as-a-Verifier: A General-Purpose Verification Framework Dario Amodei, and Paul Christiano. Learning to summarize from human feedback, February 2022. URLhttp://arxiv.org/abs/2...

    2022 arXiv

  8. [16]

    Gemini 2.5 Flash

    Google. Gemini 2.5 Flash. https://ai.google.dev/gemini-api/docs/models/gemini-2. 5-flash, 2026. Accessed: 2026-05-06

    2026

  9. [17]

    Merrill, Alexander G

    Mike A. Merrill, Alexander G. Shaw, Nicholas Carlini, Boxuan Li, Harsh Raj, Ivan Bercovich, Lin Shi, Jeong Yeon Shin, Thomas Walshe, E. Kelly Buchanan, Junhong Shen, Guanghao Ye, Haowei Lin, JasonPoulos, MaoyuWang, MariannaNezhurina, JeniaJitsev, DiLu, OrfeasMenisMastromichala...

    2026 arXiv

  10. [18]

    Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan

    Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. SWE-bench: Can Language Models Resolve Real-World GitHub Issues?, November

  11. [19]

    arXiv:2310.06770 [cs]

    URLhttp://arxiv.org/abs/2310.06770. arXiv:2310.06770 [cs]

    arXiv

  12. [20]

    YixingJiang,KameronC.Black,GloriaGeng,DannyPark,JamesZou,AndrewY.Ng,andJonathanH. Chen. A virtual ehr environment to benchmark medical llm agents.NEJM AI, 2(9), 2025. doi: 10.1056/AIdbp2500144

    2025 doi

  13. [21]

    URL https://www.tbench.ai/leaderboard/terminal-bench/2.0/capy-build/unknown/ gpt-5.5%40openai

    Capy. URL https://www.tbench.ai/leaderboard/terminal-bench/2.0/capy-build/unknown/ gpt-5.5%40openai

  14. [22]

    URL https://github.com/harbor-framework/terminal-bench/tree/main/ terminal_bench/agents/terminus_2

    Terminus 2. URL https://github.com/harbor-framework/terminal-bench/tree/main/ terminal_bench/agents/terminus_2

  15. [23]

    Vision language models are in-context value learners

    Yecheng Jason Ma, Joey Hejna, Ayzaan Wahid, Chuyuan Fu, Dhruv Shah, Jacky Liang, Zhuo Xu, Sean Kirmani, Peng Xu, Danny Driess, Ted Xiao, Jonathan Tompson, Osbert Bastani, Dinesh Jayaraman, Wenhao Yu, Tingnan Zhang, Dorsa Sadigh, and Fei Xia. Vision language models are in-conte...

    2025 doi arXiv

  16. [24]

    URLhttps://arxiv.org/abs/2410.24164

    Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, Jam...

    2026 arXiv

  17. [25]

    Qwen3 technical report.arXiv preprint arXiv:2505.09388, 2025

    An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report.arXiv preprint arXiv:2505.09388, 2025. 19 LLM-as-a-Verifier: A General-Purpose Verification Framework

    2025 arXiv

  18. [26]

    Chi, Quoc V

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models, 2022. URLhttps://arxiv.org/abs/2201.11903

    2022 arXiv

  19. [27]

    Large language models are zero-shot reasoners, 2022

    Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners, 2022. URLhttps://arxiv.org/abs/2205.11916

    2022 arXiv

  20. [28]

    Le, and Ed H

    Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, and Ed H. Chi. Least-to-most prompting enables complex reasoning in large language models. InInternational Conference on Learning Representa...

    2023

  21. [29]

    Self-consistency improves chain of thought reasoning in language models

    XuezhiWang, JasonWei, DaleSchuurmans, QuocLe, EdChi, SharanNarang, AakankshaChowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. URL http://arxiv.org/abs/2203.11171

    arXiv

  22. [30]

    Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023

    ShunyuYao, DianYu, JeffreyZhao, IzhakShafran, TomGriffiths, YuanCao, andKarthikNarasimhan. Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023

    2023

  23. [31]

    Graph of thoughts: Solving elaborate problems with large language models, 2023

    Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, and Torsten Hoefler. Graph of thoughts: Solving elaborate problems with large language models, 2023. URLhttps://a...

    2023 arXiv

  24. [32]

    ReAct: Synergizing reasoning and acting in language models

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. ReAct: Synergizing reasoning and acting in language models. URLhttp://arxiv.org/abs/2210. 03629

  25. [33]

    Lan- guage agent tree search unifies reasoning acting and planning in language models.arXiv preprint arXiv:2310.04406, 2023

    Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, and Yu-Xiong Wang. Lan- guage agent tree search unifies reasoning acting and planning in language models.arXiv preprint arXiv:2310.04406, 2023

    2023 arXiv

  26. [34]

    Reasoning with language model is planning with world model

    Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu. Reasoning with language model is planning with world model. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023. URLhttps://arxiv.org/abs/2305. 14992

    2023

  27. [35]

    Large language models are better reasoners with self-verification, 2022

    Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, and Jun Zhao. Large language models are better reasoners with self-verification, 2022. URLhttps: //arxiv.org/abs/2212.09561

    2022 arXiv

  28. [36]

    Self-refine: Iterative refinement with self-feedback, 2023

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

    2023 arXiv

  29. [37]

    Reflexion: Language agents with verbal reinforcement learning, 2023

    Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning, 2023. URLhttps: //arxiv.org/abs/2303.11366. 20 LLM-as-a-Verifier: A General-Purpose Verification Framework

    2023 arXiv

  30. [38]

    CRITIC: Large language models can self-correct with tool-interactive critiquing

    Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Nan Duan, and Weizhu Chen. CRITIC: Large language models can self-correct with tool-interactive critiquing. InInternational Conference on Learning Representations, 2024. URLhttps://arxiv.org/abs/2305.11738

    2024 arXiv

  31. [39]

    Agrawal, Shangyin Tan, Dilara Soylu, Noah Ziems, Rishi Khare, Krista Opsahl-Ong, Arnav Singhvi, Herumb Shandilya, Michael J

    Lakshya A. Agrawal, Shangyin Tan, Dilara Soylu, Noah Ziems, Rishi Khare, Krista Opsahl-Ong, Arnav Singhvi, Herumb Shandilya, Michael J. Ryan, Meng Jiang, Christopher Potts, Koushik Sen, Alexandros G. Dimakis, Ion Stoica, Dan Klein, Matei Zaharia, and Omar Khattab. GEPA: Reflec...

    2025 arXiv

  32. [40]

    Alexander Novikov, Ngân V˜u, Marvin Eisenberger, Emilien Dupont, Po-Sen Huang, Adam Zsolt Wagner, Sergey Shirobokov, Borislav Kozlovskii, Francisco J. R. Ruiz, Abbas Mehrabian, M. Pawan Kumar, Abigail See, Swarat Chaudhuri, George Holland, Alex Davies, Sebastian Nowozin, Pushm...

  33. [41]

    arXiv:2506.13131 [cs]

    URLhttp://arxiv.org/abs/2506.13131. arXiv:2506.13131 [cs]

    arXiv

  34. [42]

    Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals

    Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Ale...

    2022 doi

  35. [43]

    Le, Christopher Ré, and Azalia Mirhoseini

    Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, and Azalia Mirhoseini. Large language monkeys: Scaling inference compute with repeated sampling,

  36. [44]

    URLhttps://arxiv.org/abs/2407.21787

    arXiv

  37. [45]

    Inference-aware fine-tuning for best-of-N sampling in large language models, 2024

    Yinlam Chow, Guy Tennenholtz, Izzeddin Gur, Vincent Zhuang, Bo Dai, Sridhar Thiagarajan, Craig Boutilier, Rishabh Agarwal, Aviral Kumar, and Aleksandra Faust. Inference-aware fine-tuning for best-of-N sampling in large language models, 2024. URLhttps://arxiv.org/abs/2412.15287

    2024

  38. [46]

    GPTScore: Evaluate as you desire, 2023

    Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu. GPTScore: Evaluate as you desire, 2023. URLhttps://arxiv.org/abs/2302.04166

    2023 arXiv

  39. [47]

    G-eval: NLG evaluation using GPT-4 with better human alignment

    Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. G-eval: NLG evaluation using GPT-4 with better human alignment. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2511–2522, Singapore, December

    2023

  40. [48]

    doi: 10.18653/v1/2023.emnlp-main.153

    Association for Computational Linguistics. doi: 10.18653/v1/2023.emnlp-main.153. URL https://aclanthology.org/2023.emnlp-main.153/

    2023 doi

  41. [49]

    Xing, Hao Zhang, Joseph E

    Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging LLM-as-a-judge with MT-bench and chatbot arena. InAdvances in Neural Information Processi...

    2023

  42. [50]

    Hashimoto

    Yann Dubois, Balázs Galambosi, Percy Liang, and Tatsunori B. Hashimoto. Length-controlled AlpacaEval: A simple way to debias automatic evaluators, 2024. URLhttps://arxiv.org/abs/ 2404.04475. 21 LLM-as-a-Verifier: A General-Purpose Verification Framework

    2024 arXiv

  43. [51]

    Gonzalez, and Ion Stoica

    Tianle Li, Wei-Lin Chiang, Evan Frick, Lisa Dunlap, Tianhao Wu, Banghua Zhu, Joseph E. Gonzalez, and Ion Stoica. From crowdsourced data to high-quality benchmarks: Arena-Hard and BenchBuilder pipeline, 2024. URLhttps://arxiv.org/abs/2406.11939

    2024 arXiv

  44. [52]

    Flask: Fine-grained language model evaluation based on alignment skill sets.arXiv preprint arXiv:2307.10928, 2023

    Seonghyeon Ye, Doyoung Kim, Sungdong Kim, Hyeonbin Hwang, Seungone Kim, Yongrae Jo, James Thorne, Juho Kim, and Minjoon Seo. Flask: Fine-grained language model evaluation based on alignment skill sets.arXiv preprint arXiv:2307.10928, 2023

    2023 arXiv

  45. [53]

    Prometheus: Inducing fine-grained evaluation capability in language models

    Seungone Kim, Jamin Shin, Yejin Cho, Joel Jang, Shayne Longpre, Hwaran Lee, Sangdoo Yun, Seongjin Shin, Sungdong Kim, James Thorne, and Minjoon Seo. Prometheus: Inducing fine-grained evaluation capability in language models. InInternational Conference on Learning Representations,

  46. [54]

    URLhttps://openreview.net/forum?id=8euJaTveKw

  47. [55]

    Prometheus 2: An open source language model specialized in evaluating other language models, 2024

    Seungone Kim, Juyoung Suk, Shayne Longpre, Bill Yuchen Lin, Jamin Shin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, and Minjoon Seo. Prometheus 2: An open source language model specialized in evaluating other language models, 2024. URLhttps://arxiv.org/abs/2405. 01535

    2024

  48. [56]

    Generative judge for evaluating alignment

    Junlong Li, Shichao Sun, Weizhe Yuan, Run-Ze Fan, Hai Zhao, and Pengfei Liu. Generative judge for evaluating alignment. InInternational Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=gtkFw6sZGS

    2024

  49. [57]

    Themis: A reference-free NLG evaluation language model with flexibility and interpretability, 2024

    Xinyu Hu, Li Lin, Mingqi Gao, Xunjian Yin, and Xiaojun Wan. Themis: A reference-free NLG evaluation language model with flexibility and interpretability, 2024. URLhttps://arxiv.org/ abs/2406.18365

    2024 arXiv

  50. [58]

    HD-eval: Aligning large language model evaluators through hierarchical criteria decomposition

    Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, and Qi Zhang. HD-eval: Aligning large language model evaluators through hierarchical criteria decomposition. InProceedings of the 62nd Annual Meeting of the Association for Co...

    2024 doi

  51. [59]

    PandaLM:An automaticevaluationbenchmarkforLLMinstructiontuningoptimization

    Yidong Wang, Zhuohao Yu, Wenjin Yao, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, ChaoyaJiang, RuiXie, JindongWang, XingXie, WeiYe, ShikunZhang, andYueZhang. PandaLM:An automaticevaluationbenchmarkforLLMinstructiontuningoptimization. InInternationalConference on Learnin...

    2024

  52. [60]

    JudgeLM: Fine-tuned large language models are scalable judges

    Lianghui Zhu, Xinggang Wang, and Xinlong Wang. JudgeLM: Fine-tuned large language models are scalable judges. InInternational Conference on Learning Representations, 2025. URLhttps: //openreview.net/forum?id=xsELpEPn4A

    2025

  53. [61]

    Chateval: Towards better llm-based evaluators through multi-agent debate.arXiv preprint arXiv:2308.07201, 2023

    Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, and Zhiyuan Liu. Chateval: Towards better llm-based evaluators through multi-agent debate.arXiv preprint arXiv:2308.07201, 2023

    2023 arXiv

  54. [62]

    Shrinking the generation-verification gap with weak verifiers.arXiv preprint arXiv:2506.18203, 2025

    Jon Saad-Falcon, E Kelly Buchanan, Mayee F Chen, Tzu-Heng Huang, Brendan McLaughlin, Tanvir Bhathal, Shang Zhu, Ben Athiwaratkun, Frederic Sala, Scott Linderman, et al. Shrinking the generation-verification gap with weak verifiers.arXiv preprint arXiv:2506.18203, 2025. 22 LLM-...

    2025

  55. [63]

    Large language models are not fair evaluators

    Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Lingpeng Kong, Qi Liu, Tianyu Liu, and Zhifang Sui. Large language models are not fair evaluators. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1...

    2024 doi

  56. [64]

    Evaluating large language models at evaluating instruction following

    Zhiyuan Zeng, Jiatong Yu, Tianyu Gao, Yu Meng, Tanya Goyal, and Danqi Chen. Evaluating large language models at evaluating instruction following. InInternational Conference on Learning Representations, 2024. URLhttps://openreview.net/forum?id=tr0KidwPLc

    2024

  57. [65]

    Benchmarking cognitive biases in large language models as evaluators, 2023

    Ryan Koo, Minhwa Lee, Vipul Raheja, Jong Inn Park, Zae Myung Kim, and Dongyeop Kang. Benchmarking cognitive biases in large language models as evaluators, 2023. URLhttps://arxiv. org/abs/2309.17012

    2023 arXiv

  58. [66]

    LLMs as narcissistic evaluators: When ego inflates evaluation scores, 2023

    Yiqi Liu, Nafise Sadat Moosavi, and Chenghua Lin. LLMs as narcissistic evaluators: When ego inflates evaluation scores, 2023. URLhttps://arxiv.org/abs/2311.09766

    2023 arXiv

  59. [67]

    JudgeBench: A benchmark for evaluating LLM-based judges

    Sijun Tan, Siyuan Zhuang, Kyle Montgomery, William Yuan Tang, Alejandro Cuadron, Chenguang Wang, Raluca Ada Popa, and Ion Stoica. JudgeBench: A benchmark for evaluating LLM-based judges. InInternational Conference on Learning Representations, 2025. URLhttps://openreview. net/f...

    2025

  60. [68]

    An empirical study of LLM-as-a-judge for LLM evaluation: Fine-tuned judge model is not a general substitute for GPT-4

    Hui Huang, Xingyuan Bu, Hongli Zhou, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, and Tiejun Zhao. An empirical study of LLM-as-a-judge for LLM evaluation: Fine-tuned judge model is not a general substitute for GPT-4. InFindings of the Association for Computational Linguistics: A...

    2025

  61. [69]

    Hongchao Jiang, Yiming Chen, Yushi Cao, Hung-yi Lee, and Robby T. Tan. CodeJudgeBench: Benchmarking LLM-as-a-judge for coding tasks, 2025. URLhttps://arxiv.org/abs/2507.10535

    2025 arXiv

  62. [70]

    MLLM-as-a-judge: Assessing multimodal LLM-as-a-judge with vision-language benchmark, 2024

    Dongping Chen, Ruoxi Chen, Shilin Zhang, Yinuo Liu, Yaochen Wang, Huichi Zhou, Qihui Zhang, Yao Wan, Pan Zhou, and Lichao Sun. MLLM-as-a-judge: Assessing multimodal LLM-as-a-judge with vision-language benchmark, 2024. URLhttps://arxiv.org/abs/2402.04788

    2024 arXiv

  63. [71]

    Prometheus-vision: Vision-language model as a judge for fine-grained evaluation, 2024

    Seongyun Lee, Seungone Kim, Sue Hyun Park, Geewook Kim, and Minjoon Seo. Prometheus-vision: Vision-language model as a judge for fine-grained evaluation, 2024. URLhttps://arxiv.org/ abs/2401.06591

    2024 arXiv

  64. [72]

    LLaVA-critic: Learning to evaluate multimodal models, 2024

    Tianyi Xiong, Xiyao Wang, Dong Guo, Qinghao Ye, Haoqi Fan, Quanquan Gu, Heng Huang, and Chunyuan Li. LLaVA-critic: Learning to evaluate multimodal models, 2024. URLhttps://arxiv. org/abs/2410.02712

    2024 arXiv

  65. [73]

    Solving math word problems with process- and outcome-based feedback, 2022

    Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. Solving math word problems with process- and outcome-based feedback, 2022. URLhttps://arxiv.org/abs/2211.14275

    2022 arXiv

  66. [74]

    VIP: Towards universal visual reward and representation via value-implicit pre-training,

    Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang. VIP: Towards universal visual reward and representation via value-implicit pre-training,

  67. [75]

    23 LLM-as-a-Verifier: A General-Purpose Verification Framework

    URLhttps://arxiv.org/abs/2210.00030. 23 LLM-as-a-Verifier: A General-Purpose Verification Framework

    arXiv

  68. [76]

    LIV: Language-image representations and rewards for robotic control, 2023

    Yecheng Jason Ma, William Liang, Vaidehi Som, Vikash Kumar, Amy Zhang, Osbert Bastani, and Dinesh Jayaraman. LIV: Language-image representations and rewards for robotic control, 2023. URLhttps://arxiv.org/abs/2306.00958

    2023 arXiv

  69. [77]

    Sontakke, Jesse Zhang, Sébastien M

    Sumedh A. Sontakke, Jesse Zhang, Sébastien M. R. Arnold, Karl Pertsch, Erdem Biyik, Dorsa Sadigh, Chelsea Finn, and Laurent Itti. RoboCLIP: One demonstration is enough to learn robot policies,

  70. [78]

    URLhttps://arxiv.org/abs/2310.07899

    arXiv

  71. [79]

    Vision- language models are zero-shot reward models for reinforcement learning, 2023

    Juan Rocamonde, Victoriano Montesinos, Elvis Nava, Ethan Perez, and David Lindner. Vision- language models are zero-shot reward models for reinforcement learning, 2023. URLhttps: //arxiv.org/abs/2310.12921

    2023 arXiv

  72. [80]

    RL-VLM-F: Reinforcement learning from vision language foundation model feedback, 2024

    Yufei Wang, Zhanyi Sun, Jesse Zhang, Zhou Xian, Erdem Biyik, David Held, and Zackory Erickson. RL-VLM-F: Reinforcement learning from vision language foundation model feedback, 2024. URL https://arxiv.org/abs/2402.03681

    2024 arXiv

  73. [81]

    Language to rewards for robotic skill synthesis, 2023

    Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montse Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, Brian Ichter, Ted Xiao, Peng Xu, Andy Zeng, Tingnan Zhang, Nicolas Heess, Dorsa Sadigh, Jie Tan, Yuval Tassa, and Fei...

    2023 arXiv

  74. [82]

    Text2Reward: Reward shaping with language models for reinforcement learning, 2023

    Tianbao Xie, Siheng Zhao, Chen Henry Wu, Yitao Liu, Qian Luo, Victor Zhong, Yanchao Yang, and Tao Yu. Text2Reward: Reward shaping with language models for reinforcement learning, 2023. URLhttps://arxiv.org/abs/2309.11489

    2023 arXiv

  75. [83]

    Eureka: Human-level reward design via coding large language models, 2023

    Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Eureka: Human-level reward design via coding large language models, 2023. URLhttps://arxiv.org/abs/2310.12931

    2023 arXiv

  76. [84]

    Lim, Jesse Thomason, Erdem Biyik, and Jesse Zhang

    Jiahui Zhang, Yusen Luo, Abrar Anwar, Sumedh Anand Sontakke, Joseph J. Lim, Jesse Thomason, Erdem Biyik, and Jesse Zhang. ReWiND: Language-guided rewards teach robot policies without new demonstrations, 2025. URLhttps://arxiv.org/abs/2505.10911

    2025

  77. [85]

    SARM: Stage-aware reward modeling for long horizon robot manipulation, 2025

    Qianzhong Chen, Justin Yu, Mac Schwager, Pieter Abbeel, Yide Shentu, and Philipp Wu. SARM: Stage-aware reward modeling for long horizon robot manipulation, 2025. URLhttps://arxiv. org/abs/2509.25358

    2025 arXiv

  78. [86]

    Steering your generalists: Improving robotic foundation models via value guidance, 2024

    Mitsuhiko Nakamoto, Oier Mees, Aviral Kumar, and Sergey Levine. Steering your generalists: Improving robotic foundation models via value guidance, 2024. URLhttps://arxiv.org/abs/ 2410.13816

    2024 arXiv

  79. [87]

    Bidirectional decoding: Improving action chunking via guided test-time sampling, 2024

    Yuejiang Liu, Jubayer Ibn Hamid, Annie Xie, Yoonho Lee, Maximilian Du, and Chelsea Finn. Bidirectional decoding: Improving action chunking via guided test-time sampling, 2024. URL https://arxiv.org/abs/2408.17355

    2024 arXiv

  80. [88]

    RoboMonkey: Scaling test-time sampling and verification for vision-language- action models

    Jacky Kwok, Christopher Agia, Rohan Sinha, Matt Foutter, Shulu Li, Ion Stoica, Azalia Mirhoseini, and Marco Pavone. RoboMonkey: Scaling test-time sampling and verification for vision-language- action models. In Joseph Lim, Shuran Song, and Hae-Won Park, editors,Proceedings of ...

    2025

  81. [89]

    Unpacking failure modes of generative policies: Runtime monitoring of consistency and progress, 2024

    Christopher Agia, Rohan Sinha, Jingyun Yang, Zi-ang Cao, Rika Antonova, Marco Pavone, and Jeannette Bohg. Unpacking failure modes of generative policies: Runtime monitoring of consistency and progress, 2024. URLhttps://arxiv.org/abs/2410.04640

    2024 arXiv

  82. [90]

    Scaling verification can be more effective than scaling policy learning for vision-language- action alignment, 2026

    Jacky Kwok, Xilun Zhang, Mengdi Xu, Yuejiang Liu, Azalia Mirhoseini, Chelsea Finn, and Marco Pavone. Scaling verification can be more effective than scaling policy learning for vision-language- action alignment, 2026. URLhttps://arxiv.org/abs/2602.12281

    2026

  83. [91]

    FActScore: Fine-grained atomic evaluation of factual precision in long form text generation, 2023

    Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. FActScore: Fine-grained atomic evaluation of factual precision in long form text generation, 2023. URLhttps://arxiv.org/abs/2305.14251

    2023 arXiv

  84. [92]

    Fabbri, Chien-Sheng Wu, Wenhao Liu, and Caiming Xiong

    Alexander R. Fabbri, Chien-Sheng Wu, Wenhao Liu, and Caiming Xiong. QAFactEval: Improved QA-based factual consistency evaluation for summarization, 2021. URLhttps://arxiv.org/abs/ 2112.08542

    2021 arXiv

  85. [93]

    Potsawee Manakul, Adian Liusie, and Mark J. F. Gales. SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models, 2023. URLhttps://arxiv.org/ abs/2303.08896

    2023 arXiv

  86. [94]

    World action verifier: Self-improving world models via forward-inverse asymmetry, 2026

    Yuejiang Liu, Fan Feng, Lingjing Kong, Weifeng Lu, Jinzhou Tang, Kun Zhang, Kevin Murphy, Chelsea Finn, and Yilun Du. World action verifier: Self-improving world models via forward-inverse asymmetry, 2026. URLhttps://arxiv.org/abs/2604.01985

    2026 arXiv

  87. [95]

    A” position and one “B

    Wei-Cheng Tseng, Gashon Hussein, Yuzhu Dong, Allen Z. Ren, Lucy X. Shi, XuDong Wang, Sergey Levine,ZhaoshuoLi,JinweiGu,FlorianShkurti,Ming-YuLiu,andQuanVuong. SC3-eval: Evaluating robot foundation models via self-consistent video generation, 2026. URLhttps://arxiv.org/abs/ 260...

    2026 arXiv

Pith tools

Reviewed July 11, 2026 · model on record in the stance chip above.