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REVIEW 5 major objections 6 minor 1 cited by

Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective

T0 review · 5 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A theorem shows that response-level rewards suffice to estimate the true token-level policy gradient in LLM reinforcement learning, even when the Zero-Reward Assumption fails.

desk verdict A correct but standard REINFORCE baseline identity is dressed up as a general theorem, and the claims about PPO/GRPO extend beyond what the proof supports. read the letter →

arxiv 2506.02553 v1 pith:VUM6XX3C submitted 2025-06-03 cs.LG cs.AIcs.CL

Shenghua He , Tian Xia , Xuan Zhou , Hui Wei This is my paper
classification cs.LGcs.AIcs.CL
keywords response-levelrewardzero-rewardassumptiontoken-levelcreditassignmentpolicygradienttheoremreinforcementlearningforLLMsPPOGRPOTRePO
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 claims that in LLM fine-tuning, the policy gradient computed from the true but unknown token-level rewards can be estimated unbiasedly using only a response-level reward. This holds for REINFORCE-style and actor-critic algorithms regardless of whether the Zero-Reward Assumption is true. The result gives a common theoretical foundation to PPO, GRPO, ReMax, and RLOO, showing they already have token-level modeling capability. It also justifies focusing engineering effort on improving the response-level reward model rather than on token-level supervision.

What carries the argument

The load-bearing identity is the additive decomposition $RM(W)=\sum_{t=1}^T \gamma^{t-1} r(W_{0,t-1}, w_t)$ combined with Lemma 2, the state-dependent baseline trick. Because the policy gradient at time $t$ is multiplied by $\nabla \log \pi_\theta(w_t | W_{0,t-1})$, any function depending only on $W_{0,t-1}$ has zero expectation and can be added freely. Setting that function to the accumulated known prefix reward converts the unknown future reward into a sampled total response reward, producing an unbiased estimator.

What would settle it

Construct a response-level reward function that cannot be written as $RM(W)=\sum_{t=1}^T \gamma^{t-1} r(W_{0,t-1}, w_t)$ for any token rewards $r$ (for example, a reward that depends on token-pair interactions or on the exact order of two distant tokens), then compute the gradient estimate of Theorem 1 on a small MDP and compare it with the true policy gradient computed from known token rewards; a nonzero difference would show the unbiasedness claim fails for non-additive reward functions.

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Extended reading notes

Core claim

The central claim is Theorem 1: when the response-level reward is defined as $RM(W)=\sum_{t=1}^T \gamma^{t-1} r(W_{0,t-1}, w_t)$ for real token rewards $r$ and any discount factor $\gamma$, then the policy gradient $\nabla J(\theta)$ can be written as an expectation over response-level rewards of full trajectories sampled from the current policy. The proof uses Lemma 2, which adds a state-dependent constant $c(W_{0,t-1})$ to the per-token term without changing the expectation, and then chooses the constant to be the sum of rewards of previously generated tokens. This causes the unknown token rewards after time $t$ to be replaced by the observable response-level reward of a fresh trajectory that shares the prefix. The paper concludes that REINFORCE-based methods (GRPO, ReMax, RLOO) and actor-critic methods (PPO) estimate this quantity unbiasedly, and that their differences reduce to baseline selection and approximation error.

Load-bearing premise

The theorem holds only when the response-level reward is exactly the discounted sum of per-token rewards for some real token reward function; a learned response-level reward model is not guaranteed to have this additive decomposition, and if it does not, the prefix-reward cancellation in the proof no longer applies.

Editorial extensions

If this is right

  • GRPO, ReMax, RLOO, and PPO have unbiased token-level gradient estimates in expectation even when intermediate tokens carry nonzero task reward.
  • The remaining differences among these algorithms are baseline choice and approximation error, so the theorem gives a principled way to compare them rather than relying on empirical folklore.
  • Developers can treat the RL training algorithm as a black box and concentrate on improving the response-level reward model, since the algorithm already models token-level signals.
  • The paper's proposed TRePO algorithm directly implements Theorem 2 and removes the critic network, matching GRPO in memory use while aiming at a closer approximation of the exact policy gradient.
  • The analysis offers an explanation for why PPO often outperforms DPO: PPO uses token-level information more efficiently through its baseline, even when both are trained with response-level rewards.

Reading between the lines

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

  • The theorem's guarantee depends on the response-level reward being exactly a discounted sum of per-token rewards; a learned reward model is not guaranteed to have this decomposition, so the unbiasedness proof may not transfer to arbitrary trained reward functions.
  • If the additive decomposition fails, the prefix-reward cancellation in Lemma 2 breaks, and the extra trajectories sampled at each time step would no longer correct the gradient; measuring this gap could be done by comparing the gradient from Theorem 1 against a ground-truth token-reward gradient on a controlled task.
  • The same cancellation technique may extend to other sequential decision problems beyond language generation, wherever a terminal reward can be expressed as a discounted additive function of per-step rewards, potentially simplifying credit assignment in multi-turn agent training.
  • A practical test of the theory: on a reasoning dataset where intermediate-step correctness is independently known, compare the policy-gradient estimate from response-level rewards with the estimate computed from explicit token rewards; if they differ substantially, the response-level reward model in use is not additive.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper studies reinforcement learning for large language models under the Zero-Reward Assumption, where only the final token of a response receives a reward. It proposes a Trajectory Policy Gradient Theorem claiming that, if the response-level reward RM(W) is exactly the discounted sum of per-token rewards, the policy gradient for the true token-level objective can be unbiasedly estimated using only RM(W). The paper then argues that PPO, GRPO, ReMax, and RLOO therefore possess an inherent token-level modeling capability, proposes a new algorithm called TRePO, and surveys related RL and RL-free methods. No experimental evaluation is reported.

Significance. The paper contains a correctly derived standard policy-gradient identity for LLM token MDPs (Lemma 1) and a correct baseline-invariance result (Lemma 2), and it is transparent about the steps of its derivations. However, the main theorem is essentially the REINFORCE identity for the expected value of a full-trajectory reward: for an arbitrary response-level reward RM(W), Eq. (21) is the policy gradient of E[RM(W)], and it coincides with the true token-level objective only under the additive discounted decomposition assumed in Theorem 1. The significance of the result is therefore much smaller than the title suggests unless that decomposition is a guaranteed property of practical reward models. The transfer to GRPO and PPO is not proven, and the Theorem 2 baseline derivation contains a mathematical error. These issues undermine the paper's central claims as stated, although the core identity could be rehabilitated with a more careful formulation. TRePO is presented without experiments, so its practical promise remains untested.

major comments (5)
  1. [§3.1 (Theorem 1) and Abstract] Theorem 1 assumes RM(W)=Σ_{t=1}^T γ^{t-1} r(W_{0,t-1},w_t) for the true token rewards, but the Abstract concludes that the policy gradient can be unbiasedly estimated 'using only a response-level reward model' without stating this decomposition as a caveat. A learned or arbitrary response-level reward model is not guaranteed to satisfy this additive discounted form; if it does not, Eq. (21) is the policy gradient of E[RM(W)] rather than the gradient of the true token-level objective. Please state the decomposition as an explicit standing assumption in the abstract and in the statement of Theorem 1, or revise the claims to match the theorem that actually follows.
  2. [§3.1, Corollary 1 and Trajectory Policy Gradient Theorem proof] The transfer of Theorem 1 to GRPO and PPO is not established. Corollary 1 replaces GRPO's actual estimator, Eq. (34), by the unnormalized REINFORCE form Eq. (27); the group mean and standard deviation in Eq. (34) are not a state-dependent baseline, and the nonlinear standardization of a group containing the current response is not shown to preserve unbiasedness relative to Eq. (21). For PPO, the proof after Eq. (29) equates Q−V with rollout estimates, but PPO's GAE uses a learned critic V_φ; an inaccurate critic introduces bias that Theorem 1 does not cover. Provide explicit unbiasedness proofs for the actual GRPO and PPO estimators, or restrict the conclusion to REINFORCE-style estimators without group normalization.
  3. [§3.1, Theorem 2 proof] The derivation of the near-optimal baseline is mathematically incorrect. The proof obtains b=Cov(X1X2,X2)/Var(X2), then says one can 'assume X1 and X2 are dependent' and concludes b=EX1; the conclusion b=EX1 requires X1 and X2 to be independent. For dependent X1 and X2, Cov(X1X2,X2) is not EX1 Var(X2). Equation (28) remains an unbiased estimator because b=E_{W^{(t)}}RM(W^{(t)}) depends only on the prefix W_{0,t-1}, but the variance-reduction claim that motivates TRePO is unsupported. Correct this step, or state only that b is an admissible state-dependent baseline.
  4. [§3.1, Remark 3] The paper claims the theorem is independent of γ, but RM is defined in terms of γ, and changing γ changes the objective being optimized. Remark 3's derivation leads to Eq. (26) with a 1/γ^{t-1} factor and calls this 'undesired,' which appears to contradict the claim of independence. Clarify whether Lemma 1 and Theorem 1 use the same γ and state precisely what 'independent of γ' means.
  5. [§3.2, Algorithm 1] TRePO is not specified in enough detail for a reader to implement it. Line 15 of Algorithm 1 refers to 'the average clipped surrogate gradient loss in Eqn. (30)', but Eq. (30) is a formula for merging response-level and token-level reward models, not a loss function; no clipped surrogate loss for TRePO is defined anywhere. Please supply the actual TRePO objective and update rule, and reconcile the pseudocode with Theorem 2.
minor comments (6)
  1. [§3.1, Eqs. (24)-(25)] The notation for the continuation trajectory is inconsistent: Eq. (24) uses W^{(t)} with prefix W_{0,t-1}, while Eq. (21) and the final formula use W^{(t+1)}; to estimate V(W_{0,t}), the continuation must share the prefix W_{0,t}, so the indexing should be unified.
  2. [Table 1 and Eq. (24)] G_t(W) is defined for token t, while Eq. (24) uses G(W^{(t)}_{t,M}) without a definition; align the notation.
  3. [§4.1, Eq. (34)] The symbol 'STD' is not defined; specify whether it is the population or sample standard deviation of the group rewards.
  4. [§4.1, Eq. (36)] The RLOO baseline is written as (1/(K-1))Σ_{k=1}^K r_k, which should sum over o≠k; correct the index.
  5. [§4.2, R3HF citation] R3HF is cited as [15], but the reference list identifies [17] as R3HF; check the citation keys.
  6. [§2.1, definition of R(τ)] The definition R(τ)=Σ_{t=0}^{T-1} γ^{T-1} r_t appears to use the wrong discounting convention; this is likely a typo, but it affects the notation used in the proofs.

Circularity Check

1 steps flagged · score 3.0 of 10

Partial definitional circularity in Theorem 1's premise; no fitted-input or self-citation circularity.

  1. self definitional [Section 3.1, Theorem 1 (Eq. 21)]
    "Theorem 1. In LLM application scenarios, when the response-level reward is defined in such a form RM(W=w1w2 . . . wT ) = PT t=1 γt−1r(W0,t−1, wt) for real token rewards r(W0,t−1, wt) and some discounted reward factor γ, then the policy gradient can be unbiasedly estimated from mere response-level rewards, regardless of the values of r(W0,t−1, wt) and γ."

    The theorem's premise defines the response-level reward RM(W) to be exactly the discounted sum of the per-token rewards r(W0,t−1, wt). Therefore, the statement that response-level RM(W) suffices to estimate the token-level policy gradient is partly true by construction: the token-level information is linearly encoded in RM(W) by definition. The proof's baseline-cancellation step (Lemma 2 and Eqs. 22-25) is substantive and non-vacuous, but the advertised conclusion applies only to response-level rewards that already have this additive decomposition.

full rationale

Theorem 1 is a genuine mathematical derivation once its premise is granted: Lemma 2's state-dependent baseline cancellation and the continuation sampling in Eqs. (21)-(25) are substantive. The circularity is limited to the premise, where the response-level reward is defined as the discounted sum of the very token rewards whose recovery is claimed; any response-level reward model that is not such a sum lies outside the theorem's scope, although the abstract states the conclusion without this caveat. Corollary 1's transfer to REINFORCE-family formulas is algebraically coherent for the raw RM(W) formula; whether GRPO's group normalization and PPO's GAE critic actually match this template is a separate correctness concern, not a circular step. There are no load-bearing self-citations or imported uniqueness theorems: self-references [30,42] are contextual. Hence the score is 3, reflecting a partial definitional containment rather than a fully circular derivation.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central theorem has no fitted parameters. The free parameters listed belong to the TRePO algorithm implementation and are chosen by hand rather than fitted. The most important axiom is the reward-decomposition assumption, which makes the conclusion partly definitional. The Theorem 2 proof adds an ad hoc independence assumption that is not justified.

free parameters (4)
  • TRePO sample count M per selected step
    Algorithm 1 requires a predefined number M of full trajectories per selected step; no default or selection rule is provided.
  • TRePO selected time-step set D
    Algorithm 1 chooses D from random samples or application-specific designation, but no size or concrete selection rule is specified.
  • TRePO temperature schedule
    Algorithm 1 says to increase inference temperature from zero when sampling W^{(t)}, but the schedule is unspecified.
  • TRePO linear interpolation for non-selected steps
    Algorithm 1 estimates advantages at time steps not in D by linear interpolation using the two nearest steps in D; the interpolation details are missing.
assumptions (5)
  • standard math The score function identity E[∇ log π(a|s)] = 0 holds for the differentiable policy.
    Used in Lemma 2 to show that adding a state-dependent constant does not bias the policy gradient.
  • domain assumption LLM token generation is a deterministic transition MDP: p(W_{0,t}+w|W_{0,t-1},w)=1.
    Used in Section 3.1 to justify Lemma 1 for LLMs; standard for autoregressive generation.
  • domain assumption The response-level reward equals the discounted sum of per-token rewards: RM(W)=Σ γ^{t-1} r(W_{0,t-1},w_t).
    Explicit premise of Theorem 1; this is load-bearing for the unbiasedness result.
  • domain assumption Sampled continuations W^{(t)} with shared prefix are available from the current policy at every time step.
    Used in Theorem 1 proof and TRePO to estimate E_{W^{(t+1)}}RM; in practice this requires expensive extra sampling.
  • ad hoc to paper In Theorem 2, X1 and X2 are treated as independent to set b=E X1, despite the text saying they are dependent.
    This unjustified assumption is needed for the claimed near-optimal baseline; the covariance term is dropped without valid justification.

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Cite this review

Pith. "Pith review of Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective." pith.science (2026). https://pith.science/paper/VUM6XX3C

@misc{pith2026250602553,
  author       = {Pith},
  title        = {Pith review of: Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs: A Mathematical Perspective},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VUM6XX3C}},
  note         = {Machine review of arXiv:2506.02553}
}
read the original abstract

We study a common challenge in reinforcement learning for large language models (LLMs): the Zero-Reward Assumption, where non-terminal actions (i.e., intermediate token generations) receive zero task-specific immediate reward, while only the final token receives a reward for the entire response. This assumption arises frequently in practice, as precise token-level rewards are often difficult or infeasible to obtain in LLM applications. In this work, we provide a unifying theoretical perspective. We introduce the Trajectory Policy Gradient Theorem, which shows that the policy gradient based on true, unknown token-level rewards can be unbiasedly estimated using only a response-level reward model, regardless of whether the Zero-Reward Assumption holds or not, for algorithms in the REINFORCE and Actor-Critic families. This result reveals that widely used methods such as PPO, GRPO, ReMax, and RLOO inherently possess the capacity to model token-level reward signals, offering a theoretical justification for response-level reward approaches. Our findings pave the way for more practical, efficient LLM fine-tuning, allowing developers to treat training algorithms as black boxes and focus on improving the response-level reward model with auxiliary sub-models. We also offer a detailed analysis of popular RL and non-RL methods, comparing their theoretical foundations and practical advantages across common LLM tasks. Finally, we propose a new algorithm: Token-Reinforced Policy Optimization (TRePO), a theoretically grounded method that is simpler than PPO, matches GRPO in memory efficiency, and holds promise for broad applicability.

Figures

Figures reproduced from arXiv: 2506.02553 by the authors.

Figure 1
Figure 1. Results on the HH-RLHF test set [18]. The evaluation metrics include the OpenAssistant rewards and the win rate of models against the chosen responses and SFT model outputs. The OpenAssistant reward model is not used during the training process. Note that DPO is trained on the preference data in the dataset, while Iter DPO is trained on self-generated responses, using a reward model for labeling [PITH_FULL_IMAGE:fi… view at source ↗
Figure 2
Figure 2. Pass rate on CodeContests dataset [18]. “10@1k” means that 1000 samples will be evaluated on public tests in the problem description, and only 10 of them will be submitted for hidden tests. Only Python is used here for solving problems, while AlphaCode used both Python and C++. Potential applications of TRePO: As the derivation of TRePO is closely based on the classic RL concepts, TRePO is widely applicable to those… view at source ↗

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Forward citations

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Reference graph

Works this paper leans on

43 extracted references · 8 canonical work pages · cited by 1 Pith paper

  1. [1]

    Gpt-4o system card.arXiv preprint arXiv:2410.21276, 2024

    Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card.arXiv preprint arXiv:2410.21276, 2024

    arXiv 2024

  2. [2]

    Gemini: a family of highly capable multimodal models

    Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalk- wyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023

    arXiv 2023

  3. [3]

    An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2. 5 technical report.arXiv preprint arXiv:2412.15115, 2024

    arXiv 2024

  4. [4]

    The amazon nova family of models: Technical report and model card

    Amazon Artificial General Intelligence. The amazon nova family of models: Technical report and model card. 2024

    work page 2024

  5. [5]

    Openai o1 system card.arXiv preprint arXiv:2412.16720, 2024

    Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Alek- sander Madry, Alex Beutel, Alex Carney, et al. Openai o1 system card.arXiv preprint arXiv:2412.16720, 2024. 15 Response-Level Rewards Are All You Need for Online Reinforcement Learning in LLMs

    arXiv 2024

  6. [6]

    Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.arXiv preprint arXiv:2501.12948, 2025

    Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.arXiv preprint arXiv:2501.12948, 2025

    arXiv 2025

  7. [7]

    Proximal policy optimization algorithms.arXiv preprint arXiv:1707.06347, 2017

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms.arXiv preprint arXiv:1707.06347, 2017

    arXiv 2017

  8. [8]

    Learning to summarize with human feedback.Advances in neural information processing systems, 33:3008–3021, 2020

    Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea V oss, Alec Radford, Dario Amodei, and Paul F Christiano. Learning to summarize with human feedback.Advances in neural information processing systems, 33:3008–3021, 2020

    2020

Show all 43 references
  1. [9]

    Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022

    Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730...

    2022

  2. [10]

    Training a helpful and harmless assistant with reinforcement learning from human feedback.arXiv preprint arXiv:2204.05862, 2022

    Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. Training a helpful and harmless assistant with reinforcement learning from human feedback.arXiv preprint arXiv:2204.05862, 2022

    2022 arXiv

  3. [11]

    Remax: A sim- ple, effective, and efficient reinforcement learning method for aligning large language models.arXiv preprint arXiv:2310.10505, 2023

    Ziniu Li, Tian Xu, Yushun Zhang, Zhihang Lin, Yang Yu, Ruoyu Sun, and Zhi-Quan Luo. Remax: A sim- ple, effective, and efficient reinforcement learning method for aligning large language models.arXiv preprint arXiv:2310.10505, 2023

    arXiv

  4. [12]

    Deepseekmath: Pushing the limits of mathematical reasoning in open language models

    Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024

    2024 arXiv

  5. [13]

    Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms.arXiv preprint arXiv:2402.14740, 2024

    Arash Ahmadian, Chris Cremer, Matthias Gall ´e, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet ¨Ust¨un, and Sara Hooker. Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms.arXiv preprint arXiv:2402.14740, 2024

    2024 arXiv

  6. [14]

    Dpo meets ppo: Reinforced token optimization for rlhf.arXiv preprint arXiv:2404.18922, 2024

    Han Zhong, Guhao Feng, Wei Xiong, Xinle Cheng, Li Zhao, Di He, Jiang Bian, and Liwei Wang. Dpo meets ppo: Reinforced token optimization for rlhf.arXiv preprint arXiv:2404.18922, 2024

    2024 arXiv

  7. [15]

    Dense reward for free in reinforcement learning from human feedback.arXiv preprint arXiv:2402.00782, 2024

    Alex J Chan, Hao Sun, Samuel Holt, and Mihaela Van Der Schaar. Dense reward for free in reinforcement learning from human feedback.arXiv preprint arXiv:2402.00782, 2024

    2024 arXiv

  8. [16]

    Process reinforcement through implicit rewards.arXiv preprint arXiv:2502.01456, 2025

    Ganqu Cui, Lifan Yuan, Zefan Wang, Hanbin Wang, Wendi Li, Bingxiang He, Yuchen Fan, Tianyu Yu, Qixin Xu, Weize Chen, et al. Process reinforcement through implicit rewards.arXiv preprint arXiv:2502.01456, 2025

    2025 arXiv

  9. [17]

    R3hf: Reward redistribution for enhancing reinforcement learning from human feedback.arXiv preprint arXiv:2411.08302, 2024

    Jiahui Li, Tai-wei Chang, Fengda Zhang, Kun Kuang, and Long Chen. R3hf: Reward redistribution for enhancing reinforcement learning from human feedback.arXiv preprint arXiv:2411.08302, 2024

    2024 arXiv

  10. [18]

    Is dpo superior to ppo for llm alignment? a comprehensive study.arXiv preprint arXiv:2404.10719, 2024

    Shusheng Xu, Wei Fu, Jiaxuan Gao, Wenjie Ye, Weilin Liu, Zhiyu Mei, Guangju Wang, Chao Yu, and Yi Wu. Is dpo superior to ppo for llm alignment? a comprehensive study.arXiv preprint arXiv:2404.10719, 2024

    2024 arXiv

  11. [19]

    Reinforcement learning: An introduction

    Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. second, 2018

    2018

  12. [20]

    Analysis of on-policy policy gradient methods under the distribution mismatch.arXiv preprint arXiv:2503.22244, 2025

    Weizhen Wang, Jianping He, and Xiaoming Duan. Analysis of on-policy policy gradient methods under the distribution mismatch.arXiv preprint arXiv:2503.22244, 2025

    2025

  13. [21]

    High-dimensional continu- ous control using generalized advantage estimation, 2018

    John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. High-dimensional continu- ous control using generalized advantage estimation, 2018

    2018

  14. [22]

    Vineppo: Unlocking rl potential for llm reasoning through refined credit assignment.arXiv preprint arXiv:2410.01679, 2024

    Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, Alessandro Sordoni, Siva Reddy, Aaron Courville, and Nicolas Le Roux. Vineppo: Unlocking rl potential for llm reasoning through refined credit assignment.arXiv preprint arXiv:2410.01679, 2024

    arXiv

  15. [23]

    Gonzalez, Hao Zhang, and Ion Stoica

    Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. InProceedings of the ACM SIGOPS 29th Symposium on Operating Systems ...

    2023

  16. [24]

    Direct preference optimization: Your language model is secretly a reward model.Advances in Neural Information Processing Systems, 36:53728–53741, 2023

    Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model.Advances in Neural Information Processing Systems, 36:53728–53741, 2023. 16 Response-Level Rewa...

    2023

  17. [25]

    Sweet-rl: Training multi-turn llm agents on collaborative reasoning tasks.arXiv preprint arXiv:2503.15478, 2025

    Yifei Zhou, Song Jiang, Yuandong Tian, Jason Weston, Sergey Levine, Sainbayar Sukhbaatar, and Xian Li. Sweet-rl: Training multi-turn llm agents on collaborative reasoning tasks.arXiv preprint arXiv:2503.15478, 2025

    2025 arXiv

  18. [26]

    Multi-turn reinforcement learning from preference human feedback

    Lior Shani, Aviv Rosenberg, Asaf Cassel, Oran Lang, Daniele Calandriello, Avital Zipori, Hila Noga, Orgad Keller, Bilal Piot, Idan Szpektor, et al. Multi-turn reinforcement learning from preference human feedback. arXiv preprint arXiv:2405.14655, 2024

    2024 arXiv

  19. [27]

    Lmrl gym: Benchmarks for multi-turn reinforcement learning with language models.arXiv preprint arXiv:2311.18232, 2023

    Marwa Abdulhai, Isadora White, Charlie Snell, Charles Sun, Joey Hong, Yuexiang Zhai, Kelvin Xu, and Sergey Levine. Lmrl gym: Benchmarks for multi-turn reinforcement learning with language models.arXiv preprint arXiv:2311.18232, 2023

    2023 arXiv

  20. [28]

    Archer: Training language model agents via hierarchical multi-turn rl.arXiv preprint arXiv:2402.19446, 2024

    Yifei Zhou, Andrea Zanette, Jiayi Pan, Sergey Levine, and Aviral Kumar. Archer: Training language model agents via hierarchical multi-turn rl.arXiv preprint arXiv:2402.19446, 2024

    2024 arXiv

  21. [29]

    Offline reinforcement learning for llm multi-step reasoning.arXiv preprint arXiv:2412.16145, 2024

    Huaijie Wang, Shibo Hao, Hanze Dong, Shenao Zhang, Yilin Bao, Ziran Yang, and Yi Wu. Offline reinforcement learning for llm multi-step reasoning.arXiv preprint arXiv:2412.16145, 2024

    2024 arXiv

  22. [30]

    Plangenllms: A modern survey of llm planning capabilities.arXiv preprint arXiv:2502.11221, 2025

    Hui Wei, Zihao Zhang, Shenghua He, Tian Xia, Shijia Pan, and Fei Liu. Plangenllms: A modern survey of llm planning capabilities.arXiv preprint arXiv:2502.11221, 2025

    2025 arXiv

  23. [31]

    Mt-bench-101: A fine-grained benchmark for evaluating large language models in multi-turn dialogues.arXiv preprint arXiv:2402.14762, 2024

    Ge Bai, Jie Liu, Xingyuan Bu, Yancheng He, Jiaheng Liu, Zhanhui Zhou, Zhuoran Lin, Wenbo Su, Tiezheng Ge, Bo Zheng, et al. Mt-bench-101: A fine-grained benchmark for evaluating large language models in multi-turn dialogues.arXiv preprint arXiv:2402.14762, 2024

    2024 arXiv

  24. [32]

    Interactive evaluation for medical LLMs via task- oriented dialogue system

    Ruoyu Liu, Kui Xue, Xiaofan Zhang, and Shaoting Zhang. Interactive evaluation for medical LLMs via task- oriented dialogue system. InProceedings of the 31st International Conference on Computational Linguistics, 2025

    2025

  25. [33]

    Let’s verify step by step, 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, 2023

    2023

  26. [34]

    Unraveling rlhf and its variants: Progress and practical engineering insights

    Jian Hu. Unraveling rlhf and its variants: Progress and practical engineering insights. 2024

    2024

  27. [35]

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

    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. arXiv preprint arXiv:2211.14275, 2022

    2022 arXiv

  28. [36]

    Fine-grained human feedback gives better rewards for language model training.Advances in Neural Information Processing Systems, 36:59008–59033, 2023

    Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A Smith, Mari Ostendorf, and Hannaneh Hajishirzi. Fine-grained human feedback gives better rewards for language model training.Advances in Neural Information Processing Systems, 36:59008–59033, 2023

    2023

  29. [37]

    Beyond sparse re- wards: Enhancing reinforcement learning with language model critique in text generation.arXiv preprint arXiv:2401.07382, 2024

    Meng Cao, Lei Shu, Lei Yu, Yun Zhu, Nevan Wichers, Yinxiao Liu, and Lei Meng. Beyond sparse re- wards: Enhancing reinforcement learning with language model critique in text generation.arXiv preprint arXiv:2401.07382, 2024

    2024 arXiv

  30. [38]

    Tlcr: Token-level continuous reward for fine-grained reinforcement learning from human feedback.arXiv preprint arXiv:2407.16574, 2024

    Eunseop Yoon, Hee Suk Yoon, SooHwan Eom, Gunsoo Han, Daniel Wontae Nam, Daejin Jo, Kyoung-Woon On, Mark A Hasegawa-Johnson, Sungwoong Kim, and Chang D Yoo. Tlcr: Token-level continuous reward for fine-grained reinforcement learning from human feedback.arXiv preprint arXiv:2407...

    2024 arXiv

  31. [39]

    Math- shepherd: Verify and reinforce llms step-by-step without human annotations.arXiv preprint arXiv:2312.08935, 2023

    Peiyi Wang, Lei Li, Zhihong Shao, RX Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, and Zhifang Sui. Math- shepherd: Verify and reinforce llms step-by-step without human annotations.arXiv preprint arXiv:2312.08935, 2023

    2023 arXiv

  32. [40]

    Exploring the limit of outcome reward for learning mathematical reasoning.arXiv preprint arXiv:2502.06781, 2025

    Chengqi Lyu, Songyang Gao, Yuzhe Gu, Wenwei Zhang, Jianfei Gao, Kuikun Liu, Ziyi Wang, Shuaibin Li, Qian Zhao, Haian Huang, et al. Exploring the limit of outcome reward for learning mathematical reasoning.arXiv preprint arXiv:2502.06781, 2025

    2025 arXiv

  33. [41]

    Chatbot arena: An open platform for evaluating llms by human preference

    Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Banghua Zhu, Hao Zhang, Michael Jordan, Joseph E Gonzalez, et al. Chatbot arena: An open platform for evaluating llms by human preference. InForty-first International Conference ...

    2024

  34. [42]

    Systematic evaluation of llm-as-a-judge in llm alignment tasks: Explainable metrics and diverse prompt templates.arXiv preprint arXiv:2408.13006, 2024

    Hui Wei, Shenghua He, Tian Xia, Fei Liu, Andy Wong, Jingyang Lin, and Mei Han. Systematic evaluation of llm-as-a-judge in llm alignment tasks: Explainable metrics and diverse prompt templates.arXiv preprint arXiv:2408.13006, 2024. 17 Response-Level Rewards Are All You Need for...

    2024 arXiv

  35. [43]

    Ma-rlhf: Reinforcement learning from human feedback with macro actions.arXiv preprint arXiv:2410.02743, 2024

    Yekun Chai, Haoran Sun, Huang Fang, Shuohuan Wang, Yu Sun, and Hua Wu. Ma-rlhf: Reinforcement learning from human feedback with macro actions.arXiv preprint arXiv:2410.02743, 2024. 18

    2024 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.