Pith. sign in

REVIEW 4 major objections 5 minor 52 references

Training multi-turn agents with process-scored tree rollouts—expanding promising partial trajectories and pruning dead ends before the GRPO update—improves final performance and sample efficiency, with reported gains of +9.3 points on Froze

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review

2026-08-01 22:45 UTC pith:ZQCYESS7

load-bearing objection Honest, well-scoped rollout-allocation method (PATR) with plausible gains, but the empirical support is thinner than the headlines imply: no error bars, ablations on the eval set, and an unquantified selection bias. the 4 major comments →

arxiv 2607.15610 v1 pith:ZQCYESS7 submitted 2026-07-17 cs.CL cs.AIcs.LG

Process Reward Informed Tree Rollout for Effective Multi-Turn RL

Xintong Li , Sha Li , Yuwei Zhang , Changlong Yu , Rongmei Lin , Hongye Jin , Shuyi Guan , Xin Liu
show 3 more authors
Linwei Li Qingyu Yin Jingbo Shang
This is my paper
classification cs.CL cs.AIcs.LG
keywords multi-turn reinforcement learningprocess reward modelstree rolloutGRPOLLM agentsrollout allocationSWE-BenchFrozenLake
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Standard GRPO-style agent training samples complete trajectories independently, spending equal budget on dead-ends and promising states alike. This paper argues that long-horizon agentic rollouts are better organized as a tree: a task-adaptive process scorer rates each partial trajectory, high-scoring branches are expanded with extra continuations, mid-scoring branches survive, and low-scoring or looping branches are stopped early but kept in the training group. The claim is that this quality-aware allocation produces more informative rollout groups for the same sampling budget, so the unchanged GRPO objective learns better policies. The authors support the claim with experiments on FrozenLake and SWE-Bench, reporting consistent gains over uniform sampling and over unguided tree rollouts, with the best results from a pretrained process-reward-model scorer.

Core claim

The central discovery is that process-guided adaptive tree rollout (PATR) improves multi-turn RL without touching the policy objective: process scores only decide where to spend rollout budget, while optimization uses the original task reward. At fixed intervals, each active branch is scored in [0,1]; branches above a threshold are expanded with multiple children from the same state, moderate branches continue, and branches scoring below the active median minus a margin—or repeating the same action—are pruned. Pruned and completed branches are all retained as outcome-labeled trajectories, so the tree group still offers reward contrast for group-relative advantages. On FrozenLake, PATR raises

What carries the argument

The load-bearing component is the process scorer S_phi (Eq. 3), which maps a partial trajectory to a scalar promise score in [0,1], fed into routing rules: expansion (Eq. 4), survival, and pruning (Eq. 5). The scorer is instantiated as a heuristic, a pretrained process reward model, or an LLM judge with phase-specific prompts; in the SWE-Bench setup the judge output is blended with a hand-built trajectory heuristic. The tree-rollout loop reuses shared prefixes, replaces expanded parents with their children, and keeps every terminated branch for the GRPO group G(x). The argument works because the unmodified GRPO advantage and clipped objective (Eqs. 1-2, 9) are computed over this richer group

Load-bearing premise

The load-bearing premise is that a process scorer's score of a partial trajectory reliably predicts whether continuing that branch will lead to a successful outcome; if the score is biased, expansion and pruning allocate budget to the wrong places, and reported gains could reflect selection bias rather than better exploration.

What would settle it

A concrete test: run FrozenLake training with the same tree structure but a deliberately inverted process scorer (expand the lowest-scoring branches, prune the highest). If PATR still outperforms GRPO, the gains are not caused by process-guided selection. Similarly, replace the SWE-Bench scorer scores with random numbers while keeping the tree; if the resolved rate stays at PATR levels instead of dropping toward the random-branching baseline, the scorer is not the active ingredient.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

Share X Bluesky LinkedIn Reddit HN

If this is right

  • If PATR is right, RL practitioners can improve long-horizon agent training under a fixed sampling budget by reallocating rollouts toward scored-promising branches rather than sampling every trajectory independently.
  • Process guidance, not tree structure alone, drives the gains: the random-branching tree baseline underperforms PATR, so investing in a reliable partial-trajectory scorer pays off.
  • Keeping early-pruned and failed branches in the rollout group is beneficial; excluding them ('best-n') lowers resolved rates, so negative examples are part of the mechanism.
  • The framework's compatibility with standard GRPO means it can be layered onto existing agent RL pipelines without changing the reward or objective; it reportedly works with heuristics, pretrained PRMs, and LLM judges.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The paper's own limitation note states the pretrained PRM may favor longer responses; a natural extension is to debias the scorer or aggregate step scores differently (e.g., penalize length) to see whether SWE-Bench gains grow.
  • Selection bias is a real risk: because GRPO advantages are group-relative, a biased scorer that prunes useful branches could distort gradient estimates even if final rewards improve; an ablation with a deliberately inverted or noisy scorer would separate exploration gains from selection artifacts.
  • The keep-all result suggests pruning can be aggressive as long as the pruned branches stay in the training group, which could be exploited to cut compute further by pruning earlier.
  • The LLM-judge scorer with phase-specific prompts for diagnosis, editing, and verification is likely transferable to other tool-use domains (web navigation, embodied tasks) where step-level progress is hard to hand-specify.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes PATR, a rollout-generation framework for multi-turn agent RL. PATR replaces the independent complete-trajectory sampling of vanilla GRPO with a tree-structured rollout process that, at regular intervals, scores partial trajectories with a process scorer (heuristic, pretrained PRM, or LLM judge) and routes branches into expansion, survival, or pruning; pruned branches are retained in the rollout group. The resulting group is used for standard GRPO optimization with only the task outcome reward. Experiments on FrozenLake (Qwen2.5-0.5B/3B) and SWE-Bench Verified (Qwen3-4B) report improvements of up to +9.3 and +5.0 points over GRPO, with additional ablations on tree hyperparameters and rollout-group construction.

Significance. If the central claim holds, PATR is a useful and simple contribution: it demonstrates that process-based allocation of rollout budget can improve sample efficiency in multi-turn agentic RL without changing the policy objective, and it provides a plug-in that is compatible with standard GRPO. The paper is honest about some limitations, including the risk of PRM bias. However, the empirical support is not yet convincing because of missing variance estimates, in-sample hyperparameter selection, lack of direct validation of the process scorer as a proxy for final success, and an unspecified compute budget. The idea is worth investigating further, but the current evidence does not establish the claimed 5–9 point improvements as reliable or as unambiguously attributable to the proposed mechanism.

major comments (4)
  1. [§5.2, Tables 1 and 2] The headline improvements rest on single runs. No standard errors or multiple seeds are reported. The gap between PATR-PRM and the strongest baseline on SWE-Bench is 1.8 points (27.2 vs 25.4 for ARPO); with one trajectory per condition this difference is not distinguishable from sampling noise. Please report means and standard deviations over at least 3–5 seeds for all methods and both tasks, and state the evaluation protocol for variance.
  2. [§4.1–§4.2, Eq. (3), Routing rules] The routing rules are driven entirely by S_phi, but the paper never measures whether S_phi is a valid proxy for eventual success. The Limitations section explicitly concedes that the pretrained PRM may favor longer responses, and the FrozenLake heuristic uses an oracle-style distance measure, so the positive FrozenLake result is not evidence that the PRM/judge scores are informative in SWE. Add a scorer-validity study: e.g., correlation of S_phi with outcome reward on held-out partial trajectories, separation of success/failure score distributions, or an ablation that inverts or randomizes the score ranking.
  3. [§4.3, Eq. (9), Remark] The claim of a 'controlled and localized bias' is asserted but not backed by analysis. Because the rollout group G(x) is non-uniformly selected via expansion/survival/pruning, the GRPO advantages are computed over a selected sample; the paper does not bound or estimate the bias of the resulting policy-gradient estimator relative to uniform GRPO. Please either derive the bias, show empirically that it is small (e.g., compare gradient estimates with and without selection on identical histories), or add a compute-matched baseline with uniform expansion to control for the effect of the extra branches.
  4. [Abstract; §5.1] The phrase 'under the same training budget' is not operationalized. PATR incurs extra inference cost for the PRM/judge at every scoring checkpoint and may generate a different number of rollouts than the fixed G=8 used for GRPO and DAPO. Report total compute (e.g., wall-clock time, total environment steps, total scoring inference calls) and confirm that baselines are matched on the same budget, not only on the number of initial branches.
minor comments (5)
  1. [Algorithm 1, line 7] The expansion set is described only as 'high-scoring'; the exact rule (top-k or threshold) should be stated in the pseudocode to match the implementation and Table 3.
  2. [Eq. (8), Appendix B] The value of λ in the judge–heuristic blend is never reported; the text says the LLM judgment receives the larger weight but gives no number. Add the exact value used in the experiments.
  3. [Table 1] Define 'RLen' and 'Turns' precisely — are they averaged over all completed trajectories, or only successful ones? This affects interpretation of the efficiency claims.
  4. [Figure 2] The FrozenLake legends omit Tree-Random even though Table 1 reports it; either add the curve or remove it from the table for consistency.
  5. [§5.2] The sentence 'Tree-Random achieves a high environment-done rate but lower resolved rate' is imprecise because Tree-Random's env-done (84.4) is lower than both PATR variants (90.8/92.2). Rephrase to say that Tree-Random improves completion over flat baselines but does not convert it into resolved rate.

Circularity Check

0 steps flagged

No significant circularity: PATR's process scorer is an external input used only for rollout allocation; the policy is optimized with task rewards and evaluated on held-out benchmarks.

full rationale

The derivation chain is self-contained and non-circular. PATR's central claim is empirical: adaptive tree rollouts guided by a process scorer improve GRPO training. The process scorer S_phi is not derived from the target quantity; it is an external heuristic (FrozenLake), a pretrained PRM, or an LLM judge (SWE-Bench), and the paper explicitly states it is 'used only to decide where to allocate additional rollout budget' and 'does not replace the task reward.' The GRPO objective (Eq. 2) and advantage normalization (Eq. 9) use only task outcome rewards, so no prediction is defined in terms of the fitted input. The FrozenLake heuristic uses accumulated rewards and distance-to-goal, but that is a privileged auxiliary signal, not the final success label; the claim is not that this signal predicts itself. Self-citations (WS-GRPO, Surana et al.) appear only as related work and are not load-bearing; no uniqueness theorem is imported. The Limitations section concedes the PRM 'may favor actions that generate longer responses,' and Sec. 4.3 acknowledges a 'controlled and localized bias' in the rollout group; these are validity/efficiency concerns about scorer quality and selection bias, not circularity, because the reported improvements on held-out SWE-Bench Verified and FrozenLake are not forced by construction. Absent any Eq. X = Eq. Y reduction or fitted-parameter-as-prediction step, no circularity is present.

Axiom & Free-Parameter Ledger

8 free parameters · 4 axioms · 0 invented entities

PATR introduces no new physical or mathematical entities. Its free parameters are all rollout-scoring hyperparameters, several of which are tuned on the evaluation set (SWE-Bench Verified). The core unproven premise is that process scores predict future success, which is a domain assumption about the signal quality of partial trajectories.

free parameters (8)
  • B0 (initial branches per prompt) = 4
    Set to 4 for both tasks; chosen by hand, not derived.
  • M (children per expansion) = 2 (default)
    Ablation in Table 3 shows M=3 reduces performance; the default is tuned on the eval set.
  • K (scoring interval in turns) = 5 (FrozenLake), 13 (SWE)
    Chosen as 'fixed intervals'; ablation shows K=15 or K=20 hurts, so the value is tuned on SWE-Bench.
  • top-k (expanded branches per checkpoint) = 2
    Ablation in Table 3 shows top-k=4 reduces performance; tuned on the eval set.
  • alpha (pruning margin threshold) = 0.5
    Set in Eq. 5 by hand; no sensitivity analysis reported.
  • Theta (repeat-action termination threshold) = 6
    Chosen to stop 'repeating the same action six times'; no sensitivity analysis.
  • lambda (judge-heuristic blend weight in Eq. 8) = not reported ('LLM judgment receiving the larger weight')
    The blend weight is not specified numerically, making the scorer implementation underdetermined.
  • f_agg (PRM aggregation) = last-step score
    Among several options listed in Sec. 4.2, the paper uses the most recent step score, chosen by hand.
axioms (4)
  • domain assumption Process scores are predictive of eventual task success
    The whole routing scheme (expand, survive, prune) in Sec. 4.1 assumes that a high score at turn t correlates with a higher chance of final success. This is asserted, not proven, and is the paper's weakest premise.
  • domain assumption GRPO group-relative advantage remains a valid training signal when the rollout group is a selected tree rather than an unbiased sample
    Sec. 4.3 acknowledges a 'controlled and localized bias' and claims it is limited because actions come from the current policy, but the group-relative normalization in Eq. 9 is still computed over a biased group. Whether this preserves the intended policy gradient is not analyzed.
  • domain assumption Skywork-o1-Open-PRM and Qwen2.5-Coder-7B-Instruct provide valid process scores for SWE-Bench agent trajectories
    Sec. 5.1 and Appendix A instantiate the process scorer with these models without validating their correctness for the SWE domain; the limitations section notes the PRM may favor longer responses.
  • standard math Standard RL background: group-relative normalization reduces variance and the clipped surrogate objective is valid
    The paper relies on GRPO's established properties (Sec. 3.2) without re-deriving them; this is a background assumption, not a flaw.

reviewed 2026-08-01 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Process Reward Informed Tree Rollout for Effective Multi-Turn RL." pith.science (2026). https://pith.science/paper/ZQCYESS7

@misc{pith2026260715610,
  author       = {Pith},
  title        = {Pith review of: Process Reward Informed Tree Rollout for Effective Multi-Turn RL},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZQCYESS7}},
  note         = {Machine review of arXiv:2607.15610}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Reinforcement learning (RL) has become a key approach for training LLM agents, yet popular methods such as GRPO/RLOO rely on multiple independently sampled complete trajectories for advantage estimation. In long-horizon agentic tasks, such a uniform rollout strategy can waste budget on uninformative dead-end attempts, while promising intermediate states do not receive sufficient exploration. The multi-turn structure of agentic trajectories, with interleaved actions and observations, naturally supports organizing a trajectory group as a tree, where each turn serves as a decision point for exploration. This perspective reframes effective exploration as the problem of deciding where to branch. We propose Process-Scorer Guided Adaptive Tree Rollout (PATR), a quality-aware rollout framework for multi-turn agent RL. PATR uses task-appropriate process feedback to score partial trajectories, selectively branches from promising states, reuses shared prefixes, and conservatively stops degenerate paths to reduce wasted sampling. The resulting rollout groups remain compatible with standard policy optimization while providing more efficient exploration under the same training budget. We evaluate PATR on FrozenLake and the challenging SWE-Bench, which is largely unexplored by prior tree-rollout agent RL methods. Experiments show that PATR improves performance by up to +5.0 points on SWE-Bench and +9.3 points on FrozenLake, highlighting process-guided tree rollouts as an effective strategy for scalable multi-turn RL.

Figures

Figures reproduced from arXiv: 2607.15610 by Changlong Yu, Hongye Jin, Jingbo Shang, Linwei Li, Qingyu Yin, Rongmei Lin, Sha Li, Shuyi Guan, Xin Liu, Xintong Li, Yuwei Zhang.

Figure 1
Figure 1. Figure 1: Overview of PATR. Task-adaptive process scorer evaluates partial trajectories and guides adaptive rollout through expansion, survival, and pruning. The rollout group is optimized with standard GRPO using task rewards. level trajectory quality while preserving diverse outcomes, including failures, for policy learning. We propose Process-Scorer Guided Adaptive Tree Rollout (PATR), a cost-effective rollout ge… view at source ↗
Figure 2
Figure 2. Figure 2: Training curves on FrozenLake and SWE-Bench across methods. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Training dynamics on SWE-Bench. PATR improves the balance between reward acquisition, interaction length, and policy entropy compared with flat and unguided tree-rollout baselines. Branch Factor (M) Interval (K) Top-k Resolved Rate ↑ Varying Top-k 2 13 4 26.6 Varying Branch Factor 3 13 2 25.8 3 13 1 22.0 Varying Branch Interval 2 15 2 23.4 2 20 2 24.6 PATR-PRM (default) 2 13 2 27.2 [PITH_FULL_IMAGE:figure… view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

52 extracted references · 16 linked inside Pith

  1. [1]

    arXiv preprint arXiv:2507.19849 , year=

    Agentic reinforced policy optimization , author=. arXiv preprint arXiv:2507.19849 , year=

    Pith/arXiv arXiv

  2. [2]

    arXiv preprint arXiv:2402.03300 , year=

    Deepseekmath: Pushing the limits of mathematical reasoning in open language models , author=. arXiv preprint arXiv:2402.03300 , year=

    Pith/arXiv arXiv

  3. [3]

    arXiv preprint arXiv:2501.12948 , year=

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

    Pith/arXiv arXiv

  4. [4]

    Advances in Neural Information Processing Systems , volume=

    Group-in-group policy optimization for llm agent training , author=. Advances in Neural Information Processing Systems , volume=

  5. [5]

    arXiv preprint arXiv:2602.22817 , year=

    Hierarchy-of-groups policy optimization for long-horizon agentic tasks , author=. arXiv preprint arXiv:2602.22817 , year=

    arXiv

  6. [6]

    arXiv preprint arXiv:2509.06040 , year=

    Branchgrpo: Stable and efficient grpo with structured branching in diffusion models , author=. arXiv preprint arXiv:2509.06040 , year=

    arXiv

  7. [7]

    Advances in Neural Information Processing Systems , volume=

    Act only when it pays: Efficient reinforcement learning for llm reasoning via selective rollouts , author=. Advances in Neural Information Processing Systems , volume=

  8. [8]

    Advances in Neural Information Processing Systems , volume=

    Prorl: Prolonged reinforcement learning expands reasoning boundaries in large language models , author=. Advances in Neural Information Processing Systems , volume=

  9. [9]

    arXiv preprint arXiv:2603.03078 , year=

    RAPO: Expanding Exploration for LLM Agents via Retrieval-Augmented Policy Optimization , author=. arXiv preprint arXiv:2603.03078 , year=

    arXiv

  10. [10]

    arXiv preprint arXiv:2511.16108 , year=

    Skyrl-agent: Efficient rl training for multi-turn llm agent , author=. arXiv preprint arXiv:2511.16108 , year=

    arXiv

  11. [11]

    arXiv preprint arXiv:2412.09413 , year=

    Imitate, explore, and self-improve: A reproduction report on slow-thinking reasoning systems , author=. arXiv preprint arXiv:2412.09413 , year=

    Pith/arXiv arXiv

  12. [12]

    Advances in neural information processing systems , volume=

    Tree of thoughts: Deliberate problem solving with large language models , author=. Advances in neural information processing systems , volume=

  13. [13]

    Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

    Reasoning with language model is planning with world model , author=. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

    2023

  14. [14]

    arXiv preprint arXiv:2407.01476 , year=

    Tree search for language model agents , author=. arXiv preprint arXiv:2407.01476 , year=

    arXiv

  15. [15]

    arXiv preprint arXiv:2602.11767 , year=

    TSR: Trajectory-Search Rollouts for Multi-Turn RL of LLM Agents , author=. arXiv preprint arXiv:2602.11767 , year=

    Pith/arXiv arXiv

  16. [16]

    arXiv preprint arXiv:2603.02216 , year=

    Atpo: Adaptive tree policy optimization for multi-turn medical dialogue , author=. arXiv preprint arXiv:2603.02216 , year=

    arXiv

  17. [17]

    arXiv preprint arXiv:2510.14545 , year=

    Agentic entropy-balanced policy optimization , author=. arXiv preprint arXiv:2510.14545 , year=

    arXiv

  18. [18]

    Zong, Zefang and Chen, Dingwei and Li, Yang and Yi, Qi and Zhou, Bo and Li, Chengming and Qian, Bo and Chen, Peng and Jiang, Jie , journal=. AT \^

  19. [19]

    arXiv preprint arXiv:2508.17445 , year=

    Treepo: Bridging the gap of policy optimization and efficacy and inference efficiency with heuristic tree-based modeling , author=. arXiv preprint arXiv:2508.17445 , year=

    Pith/arXiv arXiv

  20. [20]

    arXiv preprint arXiv:2512.08153 , year=

    TreeGRPO: Tree-Advantage GRPO for Online RL Post-Training of Diffusion Models , author=. arXiv preprint arXiv:2512.08153 , year=

    arXiv

  21. [21]

    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Treerl: Llm reinforcement learning with on-policy tree search , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  22. [22]

    arXiv preprint arXiv:2603.00296 , year=

    Stepwise Penalization for Length-Efficient Chain-of-Thought Reasoning , author=. arXiv preprint arXiv:2603.00296 , year=

    arXiv

  23. [23]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Step-GRPO: Enhancing Reasoning Quality and Efficiency via Structured PRM-Based Reinforcement Learning , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  24. [24]

    International Conference on Learning Representations , volume=

    Let's verify step by step , author=. International Conference on Learning Representations , volume=

  25. [25]

    Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Math-shepherd: Verify and reinforce llms step-by-step without human annotations , author=. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  26. [26]

    arXiv preprint arXiv:2505.22312 , year=

    Skywork open reasoner 1 technical report , author=. arXiv preprint arXiv:2505.22312 , year=

    Pith/arXiv arXiv

  27. [27]

    arXiv preprint arXiv:2502.01456 , year=

    Process reinforcement through implicit rewards , author=. arXiv preprint arXiv:2502.01456 , year=

    Pith/arXiv arXiv

  28. [28]

    arXiv preprint arXiv:2509.19199 , year=

    Agentic reinforcement learning with implicit step rewards , author=. arXiv preprint arXiv:2509.19199 , year=

    arXiv

  29. [29]

    arXiv preprint arXiv:2602.17025 , year=

    WS-GRPO: Weakly-Supervised Group-Relative Policy Optimization for Rollout-Efficient Reasoning , author=. arXiv preprint arXiv:2602.17025 , year=

    arXiv

  30. [30]

    Advances in neural information processing systems , volume=

    Judging llm-as-a-judge with mt-bench and chatbot arena , author=. Advances in neural information processing systems , volume=

  31. [31]

    arXiv preprint arXiv:2110.14168 , year=

    Training verifiers to solve math word problems , author=. arXiv preprint arXiv:2110.14168 , year=

    Pith/arXiv arXiv

  32. [32]

    International Conference on Learning Representations , volume=

    Scaling LLM test-time compute optimally can be more effective than scaling parameters for reasoning , author=. International Conference on Learning Representations , volume=

  33. [33]

    nature , volume=

    Mastering the game of go without human knowledge , author=. nature , volume=. 2017 , publisher=

    2017

  34. [34]

    arXiv preprint arXiv:2411.03817 , year=

    From novice to expert: Llm agent policy optimization via step-wise reinforcement learning , author=. arXiv preprint arXiv:2411.03817 , year=

    Pith/arXiv arXiv

  35. [35]

    arXiv preprint arXiv:1606.01540 , year=

    Openai gym , author=. arXiv preprint arXiv:1606.01540 , year=

    Pith/arXiv arXiv

  36. [36]

    rllm: A framework for post-training language agents , author=

  37. [37]

    arXiv preprint arXiv:2504.07164 , year=

    R2e-gym: Procedural environments and hybrid verifiers for scaling open-weights swe agents , author=. arXiv preprint arXiv:2504.07164 , year=

    Pith/arXiv arXiv

  38. [38]

    International Conference on Learning Representations , volume=

    Swe-bench: Can language models resolve real-world github issues? , author=. International Conference on Learning Representations , volume=

  39. [39]

    5-coder technical report , author=

    Qwen2. 5-coder technical report , author=. arXiv preprint arXiv:2409.12186 , year=

    Pith/arXiv arXiv

  40. [40]

    2024 , eprint=

    Qwen2 Technical Report , author=. 2024 , eprint=

    2024

  41. [41]

    arXiv preprint arXiv:2505.09388 , year=

    Qwen3 technical report , author=. arXiv preprint arXiv:2505.09388 , year=

    Pith/arXiv arXiv

  42. [42]

    Advances in Neural Information Processing Systems , volume=

    Dapo: An open-source llm reinforcement learning system at scale , author=. Advances in Neural Information Processing Systems , volume=

  43. [43]

    European conference on machine learning , pages=

    Bandit based monte-carlo planning , author=. European conference on machine learning , pages=. 2006 , organization=

    2006

  44. [44]

    2016 , eprint=

    Prioritized Experience Replay , author=. 2016 , eprint=

    2016

  45. [45]

    2018 , eprint=

    IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures , author=. 2018 , eprint=

    2018

  46. [46]

    2026 , eprint=

    Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning , author=. 2026 , eprint=

    2026

  47. [47]

    arXiv preprint arXiv:2510.24302 , year=

    Lookahead tree-based rollouts for enhanced trajectory-level exploration in reinforcement learning with verifiable rewards , author=. arXiv preprint arXiv:2510.24302 , year=

    arXiv

  48. [48]

    arXiv preprint arXiv:2210.03629 , year=

    React: Synergizing reasoning and acting in language models , author=. arXiv preprint arXiv:2210.03629 , year=

    Pith/arXiv arXiv

  49. [49]

    Advances in neural information processing systems , volume=

    Toolformer: Language models can teach themselves to use tools , author=. Advances in neural information processing systems , volume=

  50. [50]

    Advances in neural information processing systems , volume=

    Reflexion: Language agents with verbal reinforcement learning , author=. Advances in neural information processing systems , volume=

  51. [51]

    Findings of the Association for Computational Linguistics: EACL 2026 , pages=

    Salt: Step-level advantage assignment for long-horizon agents via trajectory graph , author=. Findings of the Association for Computational Linguistics: EACL 2026 , pages=

    2026

  52. [52]

    arXiv preprint arXiv:2505.20732 , year=

    Spa-rl: Reinforcing llm agents via stepwise progress attribution , author=. arXiv preprint arXiv:2505.20732 , year=

    Pith/arXiv arXiv

This paper was first reviewed by deepseek-v4-flash on August 1, 2026.