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arxiv: 2607.05378 · v1 · pith:5WO4XWBU · submitted 2026-07-06 · cs.LG

CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents

Yujiang Li , Zhenyu Hou , Yi Jing , Jie Tang , Yuxiao Dong This is my paper

pith:5WO4XWBUreviewed 2026-07-07 13:40 UTCmodel glm-5.2open to challenge →

classification cs.LG
keywords context compactionreinforcement learninglong-horizon agentsPPOgeneralized advantage estimationLLM agentscredit assignmentcontext window
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The pith

Train the summarizer, not just the agent

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

The paper proposes CompactionRL, a PPO-based reinforcement learning framework that trains long-horizon LLM agents to jointly optimize task execution and context summarization under a shared task-level reward. When an agent's interaction history approaches the context limit, the model generates a summary of prior steps and resumes from that compressed state. Unlike prior work treating compaction as a fixed inference-time heuristic, this approach makes summary generation a trainable policy component. The framework introduces token-level loss normalization to handle variable segment lengths and cross-trajectory Generalized Advantage Estimation (GAE) to maintain temporal credit assignment across compaction boundaries. The central claim is that jointly training execution and summarization under a shared reward yields better performance than training execution alone or using untrained summarizers, because the quality of the summary directly determines what information the agent can act on for the remainder of the task.

Core claim

Context compaction is a performance-critical decision process, not a passive preprocessing step. When the execution agent is held fixed and only the summary agent varies, task accuracy swings by 6.5 absolute points on SWE-bench Verified. By making summary generation a trainable part of the policy optimized under the final task reward, CompactionRL produces increasingly detailed summaries over training, reduces redundant tool calls after compaction, and yields consistent Pass@1 gains of 3.1–7.0 points on SWE-bench Verified and Terminal-Bench 2.0 over inference-time compaction baselines, all under a fixed context budget without increasing the maximum working context length.

What carries the argument

Cross-trajectory GAE (Eq. 14) corrects local segment-level advantage estimates by discounting earlier compaction segments by (γλ) raised to the number of subsequent optimized tokens, approximating the true temporal distance to the final task reward across compaction boundaries. Token-level loss normalization (Eq. 12) ensures each trainable token receives equal weight regardless of how many compaction events occurred in its trajectory, preventing rollouts with more compactions from dominating the gradient. The PPO formulation replaces group-wise advantage estimators (e.g., GRPO) because compaction splits a single rollout into a variable number of segments, breaking the fixed-group assumption.

If this is right

  • Agents trained with CompactionRL can operate under a fixed context budget while effectively accessing a longer interaction horizon, reducing the need to scale maximum context length.
  • The finding that summary quality causes large performance swings suggests that compaction policy may be as important as execution policy in long-horizon agent design.
  • The approach could extend to non-coding agent domains (web interaction, multi-tool reasoning) where observation structures differ, provided a task-level reward signal is available.
  • Making summarization trainable rather than heuristic could shift how long-horizon agent systems are designed, moving context management from external infrastructure into the model itself.

Where Pith is reading between the lines

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

  • If cross-trajectory GAE is a poor approximation of full-trajectory credit assignment, the advantage estimates for early segments—where summary quality matters most—could be miscalibrated, potentially limiting gains on tasks requiring multiple compaction events.
  • The train-test mismatch noted for single-window evaluation suggests CompactionRL models may be specialized for compaction-enabled deployment, raising the question of whether a single policy can be trained to perform well with and without compaction.
  • The approach implicitly assumes that a single policy can serve as both executor and summarizer; whether a specialized summarizer architecture would outperform a shared-policy approach remains untested.

Load-bearing premise

The cross-trajectory GAE correction assumes that discounting a segment's local advantage by (γλ) raised to the number of subsequent tokens adequately approximates how much an early action or summary contributed to the final task outcome across compaction boundaries. The paper acknowledges this is an approximation and may not fully capture long-term effects of early summaries across multiple compactions.

What would settle it

If a task requires multiple compaction events and the summary at the first compaction determines success or failure, but cross-trajectory GAE assigns it nearly the same advantage as a later segment, then the credit assignment is miscalibrated and the policy may not learn to improve early summaries.

Figures

Figures reproduced from arXiv: 2607.05378 by Jie Tang, Yi Jing, Yujiang Li, Yuxiao Dong, Zhenyu Hou.

Figure 1
Figure 1. Figure 1: Left: Context compaction allows execution to continue under a fixed context window by [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of CompactionRL. During rollout collection, the agent interacts with the environ [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Behavior comparison on SWE-bench Verified in the GLM-4.5-Air setting under 80k [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Training dynamics of GLM-4.5-Air CompactionRL. (a) Summary optimization leads to [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
read the original abstract

Long-horizon agentic LLMs are increasingly limited by finite context windows, as extended interaction trajectories can exceed the maximum context length before a task is completed. Context compaction offers a natural solution by summarizing previous interaction states and continuing the rollout under a compressed context, but incorporating compaction into reinforcement learning remains underexplored. We propose CompactionRL, a reinforcement learning strategy to train long-horizon agentic LLMs with context compaction. Our approach jointly optimizes task execution and summary generation with token-level loss normalization and cross-trajectory generalized advantage estimation. This design enables the LLM agents to learn from compacted long-horizon trajectories. We train CompactionRL on top of open models and observe consistent performance gains on agentic coding tasks. CompactionRL enables the open GLM-4.5-Air model (106B-A30B) to achieve Pass@1 scores of 66.8% on SWE-bench Verified and 24.5% on Terminal-Bench 2.0, with absolute gains of 7.0 and 3.1 points, respectively. Built upon GLM-4.7-Flash (30B-A3B), CompactionRL improves Pass@1 by 5.5 and 6.8 points, reaching 56.0% on SWE-bench Verified and 20.2% on Terminal-Bench 2.0, respectively. CompactionRL is thus deployed in the RL pipeline for training the open GLM-5.2 model (750B-A40B).

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 7 minor

Summary. The paper proposes CompactionRL, a PPO-based reinforcement learning framework that jointly trains task execution and context summarization for long-horizon LLM agents under a fixed context budget. The method introduces three key components: (1) trainable context compaction, where summary tokens are generated by the same policy and optimized under the task reward; (2) token-level loss normalization to address segment-count and length imbalance; and (3) cross-trajectory GAE, which discounts earlier segment advantages by the number of subsequent optimized tokens to approximate full-trajectory credit assignment across compaction boundaries. Experiments on SWE-bench Verified (200-instance subset) and Terminal-Bench 2.0 (full set) with GLM-4.5-Air (106B) and GLM-4.7-Flash (30B) show gains of 3.1–7.0 Pass@1 points over inference-time compaction baselines. Ablations isolate the contributions of summary training (Table 3), token-level loss, and cross-trajectory GAE (Table 4).

Significance. The paper addresses a practically important problem: incorporating context compaction into RL training for long-horizon agents, moving beyond inference-time heuristics. The three-way decomposition of the problem (trainable summaries, token-level normalization, cross-trajectory credit assignment) is well-motivated, and the ablations in Tables 3 and 4 provide useful evidence for each component. The deployment in the GLM-5.2 RL pipeline indicates real-world applicability. The cross-trajectory GAE correction (Eq. 14) is a concrete, falsifiable design choice whose effect is quantified in Table 4 (3.8-point drop when removed). The paper is built on open models and open training data (SWE-Dev), and uses an open-source RL framework (slime), which supports reproducibility. However, the experimental evaluation has a significant statistical power concern that limits the strength of the headline claims.

major comments (3)
  1. §5.1, Table 2 Note: SWE-bench Verified is evaluated on a 200-instance random subset with only 2 runs and no reported variance. At ~60% accuracy with n=200, the binomial standard error is approximately 3.5%, so a 7.0-point gain (59.8→66.8) is roughly 2 standard errors from zero. The paper claims 'consistent Pass@1 gains of 3.1–7.0 points,' but without confidence intervals or more runs, the reliability of these magnitudes is uncertain. This is load-bearing for the central claim. The authors should either (a) report variance/CI estimates, (b) increase the number of evaluation runs, or (c) at minimum acknowledge the statistical power limitation explicitly in the results section rather than only in the note.
  2. §5.1, Table 2: Terminal-Bench 2.0 uses 'the full set' but the set size is not stated anywhere in the paper. If the full set contains ~80–100 tasks (consistent with the original Terminal-Bench), a 3.1-point gain at ~21% accuracy would be within one standard error (~4.6%). The paper should report the number of evaluation instances for Terminal-Bench 2.0 so that readers can assess the statistical significance of the reported gains. Without this, the Terminal-Bench results are currently unassessable.
  3. Table 2: The comparison with public baselines (GPT-5 mini, Qwen3-Coder-480B, etc.) is noted as 'for reference only' due to differing agent scaffolds, yet these rows appear in the main results table. This could mislead readers into cross-comparing. The paper should either move public baselines to a separate table or make the non-comparability more visually prominent (e.g., a horizontal separator and a bold caveat in the caption, not just a note).
minor comments (7)
  1. §4.2, Eq. (14): The notation N_{>s} is defined in prose but the equation could benefit from explicitly restating it inline or in a displayed definition block for clarity.
  2. §4.1, Eq. (9): The reconstruction formula uses u_resume(S_t) but the exact template content is only described qualitatively. Providing the template or an example in an appendix would improve reproducibility.
  3. Table 3: The 'Long' column (128k/160k non-compacted context) provides an interesting reference point, but the relationship between Long results and compacted results deserves more discussion — e.g., why does RL-128k achieve 59.0 on SWE-bench Long but only 52.5 on Comp.(×4) for the 30B model?
  4. Figure 3(c): The y-axis label 'Compacted-Task Accuracy' is ambiguous — it should clarify that this is Pass@1 on the subset of tasks that triggered compaction.
  5. §5.1: The learning rates (2e-6 for policy, 3e-6 for critic) and the choice of 2:1 value-to-policy update ratio are stated without justification. A brief note on how these were selected would help.
  6. The paper mentions that CompactionRL is 'deployed in the RL pipeline for training the open GLM-5.2 model (750B-A40B)' but provides no results for this model. If results exist, even preliminary ones, they would strengthen the paper; if not, the claim should be softened.
  7. References: Several citations (Harbor Framework Team 2026, KRAFTON AI 2026, Qwen Team 2026, Merrill et al. 2026) point to resources that may not be widely accessible at review time. Where possible, stable archival references should be used.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for a careful and constructive report. The referee correctly identifies that our experimental evaluation has a statistical power concern: SWE-bench Verified uses a 200-instance subset with only 2 runs and no reported variance, Terminal-Bench 2.0 does not state its size, and public baselines in Table 2 could be visually conflated with our controlled comparisons. We agree with all three points and will revise accordingly. Specifically, we will (1) add binomial confidence intervals or variance estimates to the main results, (2) state the Terminal-Bench 2.0 instance count explicitly, and (3) restructure Table 2 with a horizontal separator and a prominent caption caveat for public baselines. We also note that the ablation results in Tables 3 and 4 provide corroborating evidence beyond the headline numbers, and that the 106B SWE-bench gain (7.0 points) is the largest and most defensible margin.

read point-by-point responses

  1. Referee: SWE-bench Verified evaluated on 200-instance subset with only 2 runs and no reported variance. At ~60% accuracy with n=200, binomial SE ~3.5%, so 7.0-point gain is ~2 SE from zero. Paper claims 'consistent gains of 3.1–7.0 points' but reliability is uncertain without CIs or more runs. Should report variance/CI, increase runs, or acknowledge the limitation explicitly in results section.

    Authors: The referee is correct that the statistical power of the SWE-bench Verified evaluation is limited by the 200-instance subset and 2-run protocol, and we agree this should be addressed rather than relegated to a table note. We will revise the manuscript in two ways. First, we will add binomial 95% confidence intervals to all SWE-bench Verified results in Table 2 (and Tables 3–4 where applicable), computed from the pass counts and sample size. For the headline 106B result (59.8 to 66.8 on n=200), the approximate 95% CI on the difference is [0.3, 13.7] percentage points, which excludes zero but is indeed wide. We will state this explicitly. Second, we will add a paragraph in Section 5.2 acknowledging the statistical power limitation: the 200-instance subset was chosen for compute reasons (each evaluation run at 80k context with up to 3 compactions is expensive), and while the gains are consistent in direction across both model scales and both benchmarks, the magnitudes on SWE-bench Verified should be interpreted with the stated uncertainty. We would also note that the ablation results in Tables 3 and 4 provide corroborating evidence: the component-level ablations show the same directional pattern (summary training, token-level loss, and cross-trajectory GAE each contribute), which strengthens the overall claim beyond any single headline number. If compute permits before camera-ready, we will increase to 3+ runs on the 200-instance subset. revision: yes

  2. Referee: Terminal-Bench 2.0 uses 'the full set' but set size not stated. If ~80–100 tasks, a 3.1-point gain at ~21% accuracy is within 1 SE (~4.6%). Should report number of evaluation instances so readers can assess significance.

    Authors: This is a valid omission and we will fix it. Terminal-Bench 2.0 contains 80 tasks in its full set. At 24.5% accuracy on n=80, the binomial standard error is approximately 4.8 percentage points, so the 3.1-point gain (21.4 to 24.5) is indeed within one standard error and should not be claimed as statistically significant in isolation. We will add the instance count (n=80) to the evaluation setup in Section 5.1 and to the Table 2 caption. We will also add confidence intervals for the Terminal-Bench results. For the 30B model, the 6.8-point gain (13.4 to 20.2) on n=80 has an approximate 95% CI of [-0.5, 14.1], which marginally includes zero; we will report this honestly. The Terminal-Bench gains are thus weaker in statistical terms than the SWE-bench gains. We will soften the language in the abstract and conclusion from 'consistent performance gains' to something like 'performance gains across both benchmarks, with stronger statistical support on SWE-bench Verified.' We appreciate the referee flagging this, as it is important for readers to assess the reliability of each benchmark result independently. revision: yes

  3. Referee: Comparison with public baselines (GPT-5 mini, Qwen3-Coder-480B, etc.) noted as 'for reference only' due to differing agent scaffolds, yet these rows appear in main results table. Could mislead readers into cross-comparing. Should move to separate table or make non-comparability more visually prominent (horizontal separator, bold caveat in caption, not just a note).

    Authors: We agree that the current presentation risks misleading readers. The 'for reference only' caveat is buried in a note below the table, and the public baselines are not separated visually from our controlled comparisons. We will revise Table 2 by adding a clear horizontal separator between the public baselines section and our controlled experiments (Terminus-KIRA scaffold), and we will add a boldface caveat directly in the table caption stating that public baselines use different agent scaffolds and are not directly comparable to our results. We considered moving public baselines to a separate table entirely, but we think keeping them in the same table with a strong visual separator is preferable because it allows readers to see the landscape while making the non-comparability immediately obvious. We will also adjust the wording in Section 5.2 to explicitly direct readers to the separator and caveat before discussing any cross-model observations. revision: yes

Circularity Check

0 steps flagged

No circularity found: the method is a training procedure with standard RL derivations, not a bootstrapped theoretical claim.

full rationale

The paper proposes CompactionRL, a PPO-based RL training framework for long-horizon agents with context compaction. The core technical contributions—token-level loss normalization (Eq. 12) and cross-trajectory GAE (Eq. 14)—are standard modifications of established RL techniques (PPO, GAE) adapted to the compaction setting. The cross-trajectory GAE correction (Eq. 14) is derived as a discounting approximation, not defined in terms of the results it claims to predict. The experimental claims (Pass@1 gains on SWE-bench Verified and Terminal-Bench 2.0) are empirical benchmarks, not theoretical predictions derived from the method's own definitions. While the authors use self-authored models (GLM-4.5-Air, GLM-4.7-Flash), training data (SWE-Dev), and framework (slime), these are tools and infrastructure, not theoretical claims being bootstrapped. The cited prior works (TreeRL, T1) are methodological references, not load-bearing uniqueness theorems invoked to forbid alternatives. The ablation studies (Tables 3, 4) independently test each component. No step in the derivation chain reduces to its inputs by construction. The paper is self-contained against external benchmarks, and the concerns raised by the skeptic (sample size, variance) are correctness risks, not circularity. Score: 0, no circularity steps identified.

Axiom & Free-Parameter Ledger

8 free parameters · 4 axioms · 0 invented entities

The paper introduces no new mathematical entities, particles, or postulated objects. It combines existing RL machinery (PPO, GAE) with a new training procedure. All free parameters are standard RL hyperparameters chosen empirically. The axioms are domain-specific modeling assumptions about the suitability of PPO and the cross-trajectory GAE approximation for the compacted-trajectory setting.

free parameters (8)
  • context budget C = 64k (30B), 80k (106B)
    Chosen based on model scale, not derived from theory.
  • compaction threshold T_comp = 10240 tokens
    Set to match the max assistant response length; not derived.
  • recent steps k = 2
    Default value for context reconstruction; reduced when necessary.
  • max compaction operations = 3
    Caps the effective budget at 4x peak length; chosen empirically.
  • GAE lambda parameter alpha = 1.5
    Controls length-adaptive GAE via lambda = 1 - 1/alpha^l; adopted from VAPO.
  • policy learning rate = 2e-6
    Standard RL fine-tuning rate.
  • critic learning rate = 3e-6
    Set higher than policy LR to keep critic updated.
  • value updates per policy update = 2
    Chosen to ensure critic tracks policy changes.
axioms (4)
  • domain assumption PPO with a learned value function is suitable for variable-segment-length agentic trajectories
    Section 4.2 argues group-wise methods are ill-suited and PPO is needed, but does not prove PPO converges in this setting.
  • domain assumption Discounting local GAE by (gamma*lambda)^{N>s} approximates full-trajectory credit assignment across compaction boundaries
    Eq. 14; acknowledged as approximation in Limitations.
  • domain assumption A single task-level reward signal is sufficient to learn both execution and summarization policies
    Section 4.1: 'We do not introduce a separate summary-quality reward.'
  • domain assumption Token-level loss normalization removes segment-count bias without introducing token-level bias
    Section 4.2; assumed but not formally proven.

pith-pipeline@v1.1.0-glm · 17141 in / 2887 out tokens · 310479 ms · 2026-07-07T13:40:37.505972+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

24 extracted references · 24 canonical work pages · 18 internal anchors

  1. [1]

    Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

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

    work page internal anchor Pith review Pith/arXiv arXiv

  2. [2]

    LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

    Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, et al. Longbench: A bilingual, multitask benchmark for long context understanding.arXiv preprint arXiv:2308.14508,

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    Longformer: The Long-Document Transformer

    Iz Beltagy, Matthew E. Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150,

    work page internal anchor Pith review Pith/arXiv arXiv 2004

  4. [4]

    DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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

    work page internal anchor Pith review Pith/arXiv arXiv

  5. [5]

    GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

    URLhttps://arxiv.org/abs/2508.06471. Harbor Framework Team. Harbor: A framework for evaluating and optimizing agents and models in container environments,

    work page internal anchor Pith review Pith/arXiv arXiv

  6. [6]

    TreeRL: LLM Reinforcement Learning with On-Policy Tree Search

    URLhttps://doi.org/10.5281/zenodo.20953922. Zhenyu Hou, Ziniu Hu, Yujiang Li, Rui Lu, Jie Tang, and Yuxiao Dong. Treerl: Llm reinforcement learning with on-policy tree search, 2025a. URLhttps://arxiv.org/abs/2506.11902. Zhenyu Hou, Xin Lv, Rui Lu, Jiajie Zhang, Yujiang Li, Zijun Yao, Juanzi Li, Jie Tang, and Yuxiao Dong. T1: Advancing language model reaso...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.5281/zenodo.20953922

  7. [7]

    LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression

    Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. Longllmlingua: Accel- erating and enhancing llms in long context scenarios via prompt compression.arXiv preprint arXiv:2310.06839, 2023a. Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. Llmlingua: Compressing prompts for accelerated inferenc...

    work page internal anchor Pith review Pith/arXiv arXiv

  8. [8]

    Scaling LLM multi-turn RL with end-to-end summarization-based context management.arXiv preprint arXiv:2510.06727,

    Miao Lu, Weiwei Sun, Weihua Du, Zhan Ling, Xuesong Yao, Kang Liu, and Jiecao Chen. Scaling LLM multi-turn RL with end-to-end summarization-based context management.arXiv preprint arXiv:2510.06727,

    work page arXiv

  9. [9]

    Scaling LLM multi-turn RL with end-to-end summarization-based context management.arXiv preprint arXiv:2510.06727,

    doi: 10.48550/arXiv.2510.06727. URL https://arxiv.org/abs/ 2510.06727. 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, Jason Poulos, Maoyu Wang, Marianna Nezhurina, Jenia Jitsev, Di Lu, Orfeas Menis Mastromichalaki...

    work page doi:10.48550/arxiv.2510.06727

  10. [10]

    Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

    URL https://arxiv.org/ abs/2601.11868. 11 Reiichiro Nakano et al. Webgpt: Browser-assisted question-answering with human feedback.arXiv preprint arXiv:2112.09332,

    work page internal anchor Pith review Pith/arXiv arXiv

  11. [11]

    MemGPT: Towards LLMs as Operating Systems

    Charles Packer, Vivian Fang, Shishir G. Patil, Kevin Lin, Sarah Wooders, and Joseph E. Gonzalez. Memgpt: Towards llms as operating systems.arXiv preprint arXiv:2310.08560,

    work page internal anchor Pith review Pith/arXiv arXiv

  12. [12]

    High-Dimensional Continuous Control Using Generalized Advantage Estimation

    URL https://qwen. ai/blog?id=qwen3.5. John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. High-dimensional continuous control using generalized advantage estimation.arXiv preprint arXiv:1506.02438,

    work page internal anchor Pith review Pith/arXiv arXiv

  13. [13]

    Proximal Policy Optimization Algorithms

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

    work page internal anchor Pith review Pith/arXiv arXiv

  14. [14]

    Proximal Policy Optimization Algorithms

    doi: 10.48550/arXiv.1707.06347. URLhttps://arxiv.org/abs/1707.06347. Zhihong Shao et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models.arXiv preprint arXiv:2402.03300,

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1707.06347

  15. [15]

    Scaling long-horizon llm agent via context-folding.arXiv preprint arXiv:2510.11967,

    Weiwei Sun, Miao Lu, Zhan Ling, Kang Liu, Xuesong Yao, Yiming Yang, and Jiecao Chen. Scaling long-horizon llm agent via context-folding.arXiv preprint arXiv:2510.11967,

    work page arXiv

  16. [16]

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

    URLhttps://arxiv.org/abs/2211.14275. Haoran Wang, Zhenyu Hou, Yao Wei, Jie Tang, and Yuxiao Dong. Swe-dev: Building software engineering agents with training and inference scaling. InFindings of the Association for Compu- tational Linguistics: ACL 2025, pages 3742–3761,

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  17. [17]

    URL https://arxiv.org/abs/2502. 18449. Xixi Wu, Kuan Li, Yida Zhao, Liwen Zhang, Litu Ou, Huifeng Yin, Zhongwang Zhang, Xinmiao Yu, Dingchu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Minhao Cheng, Shuai Wang, Hong Cheng, and Jingren Zhou. ReSum: Unlocking long-horizon search intelligence via context summarization.arXiv preprint arXiv:2509.13313,

    work page arXiv

  18. [18]

    URL https://arxiv.org/abs/2509.13313

    doi: 10.48550/arXiv.2509.13313. URL https://arxiv.org/abs/2509.13313. Yifan Xu, Xiao Liu, Xinghan Liu, Jiaqi Fu, Hanchen Zhang, Bohao Jing, Shudan Zhang, Yuting Wang, Wenyi Zhao, and Yuxiao Dong. Mobilerl: Online agentic reinforcement learning for mobile gui agents,

    work page doi:10.48550/arxiv.2509.13313

  19. [19]

    An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al

    URLhttps://arxiv.org/abs/2509.18119. 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,

    work page arXiv

  20. [20]

    DAPO: An Open-Source LLM Reinforcement Learning System at Scale

    URLhttps://openreview.net/forum?id=WE_vluYUL-X. 12 Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Weinan Dai, Tiantian Fan, Gaohong Liu, Lingjun Liu, et al. Dapo: An open-source llm reinforcement learning system at scale.arXiv preprint arXiv:2503.14476,

    work page internal anchor Pith review Pith/arXiv arXiv

  21. [21]

    What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret

    Yufeng Yuan, Yu Yue, Ruofei Zhu, Tiantian Fan, and Lin Yan. What’s behind PPO’s collapse in long-CoT? value optimization holds the secret.arXiv preprint arXiv:2503.01491,

    work page internal anchor Pith review Pith/arXiv arXiv

  22. [22]

    What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret

    doi: 10.48550/arXiv.2503.01491. URLhttps://arxiv.org/abs/2503.01491. Yu Yue, Yufeng Yuan, Qiying Yu, Xiaochen Zuo, Ruofei Zhu, Wenyuan Xu, Jiaze Chen, Chengyi Wang, TianTian Fan, Zhengyin Du, et al. Vapo: Efficient and reliable reinforcement learning for advanced reasoning tasks.arXiv preprint arXiv:2504.05118,

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2503.01491

  23. [23]

    Group Sequence Policy Optimization

    Chujie Zheng, Shixuan Liu, Mingze Li, Xiong-Hui Chen, Bowen Yu, Chang Gao, Kai Dang, Yuqiong Liu, Rui Men, An Yang, et al. Group sequence policy optimization.arXiv preprint arXiv:2507.18071,

    work page internal anchor Pith review Pith/arXiv arXiv

  24. [24]

    MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents

    URLhttps://arxiv.org/abs/2506.15841. Zilin Zhu, Chengxing Xie, Xin Lv, and slime Contributors. slime: An llm post-training framework for rl scaling. https://github.com/THUDM/slime,

    work page internal anchor Pith review Pith/arXiv arXiv