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REVIEW 4 major objections 4 minor 23 references

Learning from Environmental Feedback: Credit Assignment across Multiple Timescales for Agentic Reinforcement Learning

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read EFCA reweights per-step returns with environment feedback, improving ALFWorld and WebShop scores.

desk verdict A plausible plug-in credit assignment idea whose headline numbers are undermined by a duplicated 7B baseline row and benchmark-tuned hyperparameters. read the letter →

arxiv 2608.08255 v1 pith:EN3VTK7E submitted 2026-08-08 cs.LG cs.CL

Yifu Huo , Shunjie Xing , Chenglong Wang , Peinan Feng , Qiaozhi He , Yan Ding , Anxiang Ma , Yuxin Gao
show 3 more authors
Tongran Liu Tong Xiao Jingbo Zhu
This is my paper · ORCID
classification cs.LGcs.CL
keywords creditassignmentagenticreinforcementlearningenvironmentfeedbackstepwisepolicyoptimizationlong-horizontasksreturnreweightingLLMagentsALFWorld
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

Long-horizon agentic reinforcement learning suffers from sparse, delayed rewards, so step-level credit must be reassigned from final outcomes. The paper argues that environments already emit process feedback during interaction—what the action did, whether it changed state, whether it repeated a failure—and that this feedback is an underexploited source of credit. It proposes EFCA (Environmental Feedback-based Credit Assignment), which adds a short-term feedback credit and a medium-term state-history credit to the base step return, reweighting returns so locally effective actions get stronger supervision and ineffective loops get weaker supervision. On ALFWorld and WebShop, across 1.5B and 7B backbones, the paper reports best overall ALFWorld scores (95.31 and 96.03) and best WebShop Task Scores (89.81 and 89.06) among the compared stepwise RL methods. If these results hold, critic-free agentic RL can be improved simply by using the feedback the environment already provides.

What carries the argument

The load-bearing object is the environment-grounded multi-timescale credit score $C_t = \alpha c^{\mathrm{fb}}_t + \beta c^{\mathrm{hist}}_t$, converted into a step-dependent return weight. The short-term feedback credit $c^{\mathrm{fb}}_t$ is computed by normalizing the environment's textual feedback and testing it against a hand-constructed positive pattern set $P$ (for example, 'you pick up') and negative pattern set $N$ (for example, 'nothing happens'), returning $+1$, $-1$, or $0$. The medium-term state-history credit $c^{\mathrm{hist}}_t$ looks back over a window of $K$ steps: a window of all negative credits returns $-1$, a full window with no positive credit returns a milder $-\eta$, and otherwise it returns $0$. These credits are combined and mapped through a clipped affine weight $w_t = \mathrm{clip}(1 + \lambda C_t, w_{\min}, w_{\max})$ that multiplies the base discounted return. This lets EFCA add process-level supervision to existing stepwise optimizers without any value network or learned reward model.

What would settle it

Re-run the HGPO baseline on the Qwen2.5-7B model using the same rollout group size, memory window, and random seeds as EFCA; if the reproduced baseline differs from the 1.5B numbers currently copied into the 7B row of Table 1, or if EFCA no longer beats it by the reported margins, then the claimed consistent improvement over strong baselines is not established.

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Extended reading notes

Core claim

EFCA's central claim is that the base step-level return $R^{\mathrm{base}}_t = \sum_{t'=t}^T \gamma^{t'-t} r_{t'}$, used by critic-free stepwise optimizers, is an incomplete credit signal because it only reflects long-term outcome. The paper's mechanism reweights this return with environment-grounded process evidence: normalized textual feedback is matched against pattern sets to give $c^{\mathrm{fb}}_t \in \{-1,0,+1\}$, and a $K$-step window of these values produces $c^{\mathrm{hist}}_t$, which penalizes consecutive failures or a window with no positive progress. The combined score $C_t = \alpha c^{\mathrm{fb}}_t + \beta c^{\mathrm{hist}}_t$ is turned into a clipped weight $w_t = \mathrm{clip}(1 + \lambda C_t, w_{\min}, w_{\max})$, applied as $\tilde{R}_t = w_t R^{\mathrm{base}}_t$. On ALFWorld and WebShop with 1.5B and 7B backbones, the paper reports best overall ALFWorld scores (95.31 and 96.03) and best WebShop Task Scores (89.81 and 89.06) among the compared stepwise RL methods, with ablations showing the two signals are complementary.

Load-bearing premise

The central comparison assumes the baseline numbers were produced under the same model, rollout settings, and evaluation protocol as EFCA, and Table 1's identical HGPO rows across the two model sizes suggest that assumption was not verified.

Editorial extensions

If this is right

  • Stepwise RL optimizers can be improved by reweighting their step returns with environment feedback, without changing their grouping or optimization logic.
  • Both task success and task quality can improve together, as seen in the WebShop Task Score, which measures purchase quality rather than just completion.
  • Penalizing repeated ineffective patterns through the medium-term history signal contributes a separate, measurable gain; ablations show about a four-point drop when either signal is removed.
  • The reported gains hold at two model scales (1.5B and 7B), so the mechanism is not tied to one backbone capacity.
  • The added computation is small: pattern matching and reweighting add negligible cost, with lower reported wall-clock time per training step than the HGPO baseline.

Reading between the lines

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

  • A natural extension would replace hand-built pattern sets with semantic feedback understanding, so the credit signal transfers across environments with different feedback vocabularies; the paper lists this as future work rather than testing it.
  • Because EFCA only modifies return computation, the same reweighting scheme could probably be added to other critic-free policy optimization pipelines beyond the stepwise methods tested.
  • The state-history penalty could in principle suppress actions that are locally unproductive but globally necessary, such as exploration or enabling actions; testing this trade-off would require a causal or hierarchical credit model the paper does not provide.
  • The reported 6.36% wall-clock saving is suggestive but not a controlled causal comparison; a dedicated head-to-head with identical rollout scheduling would determine whether feedback matching itself is faster.
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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

4 major / 4 minor

Summary. The paper proposes Environmental Feedback-based Credit Assignment (EFCA), a plug-in return-reweighting mechanism for critic-free stepwise policy optimization. EFCA defines a short-term feedback credit c_t^fb by matching normalized environment feedback against hand-curated positive and negative pattern sets P and N, and a medium-term state-history credit c_t^hist based on a K-step window of those feedback credits (Eqs. (3)-(6)). The two credits are combined as C_t = alpha c_t^fb + beta c_t^hist and converted into a step weight w_t = clip(1 + lambda C_t, w_min, w_max), which multiplies the original discounted step return (Eqs. (7)-(8)). The method is evaluated on ALFWorld and WebShop with Qwen2.5-1.5B and 7B backbones, with the headline claim that EFCA consistently improves task success and task quality over GiGPO, GraphGPO, and HGPO baselines (Section 4.2.1).

Significance. If fully supported, EFCA would be a valuable lightweight alternative to learned critics and reward models for long-horizon agentic RL: it is simple, transparent, environment-grounded, and adds negligible wall-clock overhead, as the reported runtime comparison suggests. The paper includes clear ablations separating the two process signals and provides pseudo-code that makes the algorithm easy to reimplement. However, the central empirical support is weakened by three problems: a duplicated Qwen2.5-7B HGPO baseline row in Table 1; hyperparameter values selected on the same ALFWorld benchmark used for the headline results; and reused baseline numbers whose training configurations are not shown to match those of EFCA. Because the reported gains over HGPO at 7B rest on the duplicated row, the quantitative claims are only partially supported as written.

major comments (4)
  1. [Table 1 / Section 4.2.1] The Qwen2.5-7B-Instruct HGPO row is numerically identical to the Qwen2.5-1.5B-Instruct HGPO row for every reported metric, including the standard deviations (97.53/81.20/99.60/100.00/85.63/82.57/91.99 and 85.56/71.54). Since Section 4.2.1 uses this row to claim +4.04 ALFWorld and +3.50 WebShop Task Score improvements over HGPO at the 7B scale, those specific comparisons are unsupported. The row must be replaced by an actual 7B HGPO run or the corresponding claims must be removed. The caption's marker scheme (dagger vs. double dagger) also needs clarification for this row, because it is not clear which numbers were taken from the GraphGPO paper and which from the HGPO paper.
  2. [Section 4.3.2 / Figure 2 / Table 2] The main ALFWorld results appear to use hyperparameters selected on the same benchmark that is reported as the headline result. The ablation sweeps (lambda, beta, alpha in Figure 2; K, eta, w_min, w_max in Table 2) are conducted on ALFWorld, and the best-performing values are then used for the main comparison. No held-out validation split or multiple-testing correction is described. This post-hoc selection inflates the reported improvements; the authors should either fix the hyperparameters before evaluation, use a validation split, or report results across the full sweep and show that the conclusion is robust.
  3. [Section 4.1.2 / Section 4.1.3] The paper states that 'all RL-based methods share the same hyperparameter configuration' while also saying that part of the baseline results are directly reused from the GraphGPO and HGPO papers. These statements are incompatible: borrowed results were produced under the original papers' training budgets, rollout group sizes, memory settings, seeds, and evaluation protocols. Without evidence that those settings match EFCA's setup, the reported gaps between EFCA and borrowed baselines may be due to training-configuration differences rather than to credit assignment. The authors should either rerun all baselines in the same pipeline or list the exact configuration used for each borrowed result.
  4. [Abstract / Section 4.2.1] The claim that EFCA 'consistently improves both task success and task quality over strong baselines' is stronger than Table 1 actually supports. On WebShop, EFCA's task success rate is lower than GraphGPO at both scales (75.91 vs. 78.65 at 1.5B; 78.91 vs. 80.31 at 7B), and on several ALFWorld subcategories EFCA underperforms GraphGPO (e.g., Look 81.32 vs. 100.0 at 1.5B; Pick 97.93 vs. 100.0 at 7B). The wording should be qualified to comparisons on overall ALFWorld score and WebShop Task Score, or the authors should show a statistical test supporting the stronger claim.
minor comments (4)
  1. [Section 4.3.1 / Figure 4] The increased standard deviation of the reweighted returns is a mechanical consequence of multiplying all step returns by a non-constant weight, so by itself it does not demonstrate that credit assignment is more discriminative. The interpretive claim should be softened or supported by an analysis stratified by successful and failed trajectories.
  2. [Appendix B.3 / Table 2] The hyperparameter table gives 'typical / tested values' but does not state the exact values used in the main experiments. Please report the final K, eta, w_min, w_max, alpha, beta, and lambda for each benchmark and backbone, and also make the P and N pattern sets available for reproducibility.
  3. [Table 1 caption] The footnote markers are difficult to follow: the 1.5B HGPO row is marked with a dagger, which the caption attributes to the GraphGPO paper, while the 7B HGPO row is marked with a double dagger, attributed to the HGPO paper. The caption should clarify which original source supplied each row or block of entries.
  4. [Section 4.3.2] In the ablation text, 'removing the medium-term state-history signal' and 'disabling the short-term feedback signal' correspond to beta = 0 and alpha = 0 in Figure 2, respectively; this correspondence should be stated explicitly in the text.

Circularity Check

1 steps flagged · score 4.0 of 10

One fitted-input issue: the headline ALFWorld score is the best point of a hyperparameter sweep on the same benchmark; no load-bearing self-citation; the WebShop result and the core mechanism retain independent content.

  1. fitted input called prediction [Section 4.2.1 (Table 1) and Section 4.3.2 (Ablation Study, Figure 2)]
    "With Qwen2.5-1.5B-Instruct, EFCA reaches an overall ALFWorld score of95.31, outperforming HGPO by+3.32points, GiGPO by+4.43points, and GraphGPO by+2.60points. ... For the medium-term state-history coefficient, increasing it from 0 to 0.8 improves performance from 90.95 to95.31, confirming the value of suppressing repeated low-contribution actions."

    The same 95.31 value serves both as the best point of the ablation sweep and as EFCA's headline ALFWorld result. The ablation is conducted on the same ALFWorld overall score that Table 1 reports, with no separate validation set described, so the main result is the argmax over the tested hyperparameter grid rather than an independent prediction. The improvement over HGPO and GraphGPO is therefore partly selected by construction on the evaluation benchmark. This is a partial fit-to-result circularity, not a full identity: the WebShop task score and the reweighting mechanism itself are not absorbed by this selection.

full rationale

The paper contains no theoretical derivation whose output is equivalent to its input by construction; EFCA is defined as a reweighting of an existing step return (Eqs. 2, 7, 8) and its contribution is empirical. The one load-bearing circular concern is that the headline ALFWorld score (95.31) is also the optimum of the hyperparameter ablation in Section 4.3.2 run on the same benchmark, so that gain is partly fitted. Section E lists limitations but does not disclose that the main ALFWorld number is the best point of a same-benchmark sweep. The identical Qwen2.5-7B HGPO row in Table 1 and the reuse of reported baseline numbers are correctness and comparability risks, not circularity, and are not counted in the score. The self-citations to the authors' prior work (e.g., Huo et al. 2026, Wang et al. 2023a/2025a/2026a) are contextual and not load-bearing for the central claim. The WebShop best Task Score and the internal ablations give the method independent empirical content, so the overall circularity score is moderate at 4.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

EFCA is an empirical method with no formal derivation. It assumes that hand-built pattern sets can separate creditworthy from non-creditworthy feedback, that a K-step window without positive feedback indicates stagnation, and that reweighting returns preserves the validity of the underlying stepwise optimizer. The evaluation additionally assumes borrowed baselines are comparable, an assumption contradicted by the duplicated 7B HGPO row. Several coefficients were tuned on the evaluation benchmarks, so the validation includes fitted components. No new physical entities are introduced.

free parameters (7)
  • alpha (feedback-credit coefficient) = 1.5 (best in ablation, Figure 2c)
    Scales the short-term feedback credit in Eq. (2); swept on ALFWorld without a validation split.
  • beta (state-history coefficient) = 0.8 (best in ablation, Figure 2b)
    Scales the medium-term state-history credit in Eq. (2); swept on ALFWorld.
  • lambda (reweighting strength) = 0.4 (best in ablation, Figure 2a)
    Controls overall reweighting in Eq. (7); swept on ALFWorld.
  • K (state-history window size) = not explicitly stated for main runs (tested 2, 3, 5)
    Defines window W_t^K in Eq. (4); affects when stagnation penalty triggers.
  • eta (stagnation penalty) = 0.5 (Table 2)
    Sets c_hist = -eta in Eq. (6); chosen by hand, no ablation reported.
  • wmin/wmax (clipping bounds) = 0.2/0.3 and 1.8/2.0 (tested ranges)
    Clip the reweighting weight in Eq. (7); exact values for main runs not given.
  • positive/negative feedback pattern sets P,N = hand-curated from environment messages; full lists not provided
    Determine c_fb in Eq. (3); central to the short-term signal and not automatically derived.
assumptions (4)
  • ad hoc to paper The hand-curated positive and negative pattern sets P and N accurately classify local progress and ineffectiveness from environment feedback.
    Section 3.3 and Appendix B.4 define c_fb through substring matching against P and N; the sets are manually constructed from environment messages, so their adequacy is assumed.
  • domain assumption A window of K consecutive steps with no positive feedback indicates stagnation and deserves a negative credit.
    Section 3.4 defines c_hist for |W_t^K|=K and N_t^+=0; this presumes useful actions always produce detectable positive feedback within K steps, which the authors admit may not hold for exploration steps.
  • domain assumption Reweighted step-level returns w_t * R_base_t remain valid optimization targets for the underlying stepwise optimizer.
    Section 3.5 and Eq. (8) rescale returns without proof that the policy gradient estimator remains unbiased or stable; clipping bounds are used to contain damage.
  • domain assumption Baseline results from earlier GraphGPO and HGPO papers were obtained under settings comparable to EFCA's runs.
    Section 4.1.2 states results are reused from original papers; the duplicated 7B HGPO row in Table 1 indicates this assumption may be violated.

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Cite this review

Pith. "Pith review of Learning from Environmental Feedback: Credit Assignment across Multiple Timescales for Agentic Reinforcement Learning." pith.science (2026). https://pith.science/paper/EN3VTK7E

@misc{pith2026260808255,
  author       = {Pith},
  title        = {Pith review of: Learning from Environmental Feedback: Credit Assignment across Multiple Timescales for Agentic Reinforcement Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EN3VTK7E}},
  note         = {Machine review of arXiv:2608.08255}
}
read the original abstract

Agentic reinforcement learning (RL) often suffers from delayed and sparse rewards in real-world environments. A promising solution to this challenge is credit assignment, which aims to decompose trajectory-level rewards and provide more fine-grained supervision for intermediate decisions. However, existing credit assignment approaches ignore the rich process information naturally generated during environment interaction, e.g., interaction history. We argue that such information provides valuable supervision for identifying the contribution of individual actions. To this end, we propose Environmental Feedback-based Credit Assignment (EFCA), a multi-timescale credit assignment approach for long-horizon agentic RL. EFCA complements the long-term outcome signal with two environment-grounded process signals: a short-term feedback signal that captures the immediate effect of the current action and a medium-term state-history signal that identifies ineffective patterns from recent interactions. Both signals are directly extracted from environment feedback and integrated through a return reweighting mechanism. Experiments on ALFWorld and WebShop demonstrate that EFCA consistently improves both task success and task quality over strong baselines, highlighting the effectiveness of environment-grounded multi-timescale credit assignment for long-horizon agentic RL.

Figures

Figures reproduced from arXiv: 2608.08255 by the authors.

Figure 1
Figure 1. Overview of EFCA. EFCA complements the long-term outcome signal with a short [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Ablation results of EFCA on ALFWorld under different hyperparameter settings. The [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Mean reweighting ratio over training steps. The shaded area [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Standard deviation of base and reweighted step returns over [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Word clouds of ALFWorld feedback patterns used to construct the positive set [PITH_FULL_IMAGE:figures/full_fig_p017_5.png]
Figure 6
Figure 6. Figure 6: Wall-clock time per training step for EFCA and HGPO. Thin lines show raw step-level [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]

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Works this paper leans on

23 extracted references · 1 canonical work pages

  1. [1]

    Test-time adaptation for llm agents via environment interaction

    Arthur Chen, Zuxin Liu, Jianguo Zhang, Akshara Prabhakar, Zhiwei Liu, Shelby Heinecke, Silvio Savarese, Victor Zhong, and Caiming Xiong. Test-time adaptation for llm agents via environment interaction. 2025a. URL https://api.semanticscholar.org/CorpusID:282890178. Guoxin Chen, Zile Qiao, Xuanzhong Chen, Donglei Yu, Haotian Xu, Wayne Xin Zhao, Ruihua Song,...

    arXiv

  2. [4]

    Lang Feng, Zhenghai Xue, Tingcong Liu, and Bo An

    URL https://api.semanticscholar.org/ CorpusID:276937549. Lang Feng, Zhenghai Xue, Tingcong Liu, and Bo An. Group-in-group policy optimization for llm agent training.ArXiv, abs/2505.10978,

    arXiv

  3. [5]

    Hiroki Furuta, Ofir Nachum, Kuang-Huei Lee, Yutaka Matsuo, Shixiang Shane Gu, and Izzed- din Gur

    URL https://api.semanticscholar.org/CorpusID: 278715074. Hiroki Furuta, Ofir Nachum, Kuang-Huei Lee, Yutaka Matsuo, Shixiang Shane Gu, and Izzed- din Gur. Multimodal web navigation with instruction-finetuned foundation models.ArXiv, abs/2305.11854,

    arXiv

  4. [7]

    Shuo He, Lang Feng, Qi Wei, Xin Cheng, Lei Feng, and Bo An

    URL https://api.semanticscholar.org/CorpusID: 260126067. Shuo He, Lang Feng, Qi Wei, Xin Cheng, Lei Feng, and Bo An. Hierarchy-of-groups pol- icy optimization for long-horizon agentic tasks.ArXiv, abs/2602.22817,

    arXiv

  5. [8]

    SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models

    URL https: //api.semanticscholar.org/CorpusID:286083468. Yifu Huo, Chenglong Wang, Ziming Zhu, Shunjie Xing, Peinan Feng, Tongran Liu, Qiaozhi He, Tianhua Zhou, Xiao-Wen Chang, Jingbo Zhu, Zhengtao Yu, and Tong Xiao. Sps: Steering proba- bility squeezing for better exploration in reinforcement learning for large language models.ArXiv, abs/2604.16995,

    work page Pith review arXiv

  6. [9]

    Barrett Martin Lattimer, Varun Gangal, Ryan Mcdonald, and Yi Yang

    URL https://api.semanticscholar.org/CorpusID: 287950328. Barrett Martin Lattimer, Varun Gangal, Ryan Mcdonald, and Yi Yang. Sparse rewards can self-train dialogue agents.ArXiv, abs/2409.04617,

    arXiv

  7. [11]

    Hunter Lightman, Vineet Kosaraju, Yura Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe

    URL https://api.semanticscholar.org/CorpusID:273234205. Hunter Lightman, Vineet Kosaraju, Yura Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let’s verify step by step.ArXiv, abs/2305.20050,

    arXiv

  8. [12]

    Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, and Laura Toni

    URL https://api.semanticscholar.org/CorpusID:258987659. Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, and Laura Toni. A survey of temporal credit assignment in deep reinforcement learning.ArXiv, abs/2312.01072,

    arXiv

Show all 23 references
  1. [14]

    11 Timo Schick, Jane Dwivedi-Yu, Roberto Dess `ı, Roberta Raileanu, Maria Lomeli, Luke Zettle- moyer, Nicola Cancedda, and Thomas Scialom

    URL https://api.semanticscholar.org/CorpusID: 274859421. 11 Timo Schick, Jane Dwivedi-Yu, Roberto Dess `ı, Roberta Raileanu, Maria Lomeli, Luke Zettle- moyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach them- selves to use tools.ArXiv, abs/2302.04761,

    arXiv

  2. [15]

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov

    URL https://api.semanticscholar.org/CorpusID: 256697342. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms.ArXiv, abs/1707.06347,

    arXiv

  3. [17]

    Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Cˆot´e, Yonatan Bisk, Adam Trischler, and Matthew J

    URL https://api.semanticscholar.org/CorpusID:258833055. Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Cˆot´e, Yonatan Bisk, Adam Trischler, and Matthew J. Hausknecht. Alfworld: Aligning text and embodied environments for interactive learning.ArXiv, abs/2010.03768,

    2010 arXiv

  4. [19]

    Chenglong Wang, Hang Zhou, Yimin Hu, Yi Huo, Bei Li, Tongran Liu, Tong Xiao, and Jingbo Zhu

    URL https://api.semanticscholar.org/CorpusID:286424372. Chenglong Wang, Hang Zhou, Yimin Hu, Yi Huo, Bei Li, Tongran Liu, Tong Xiao, and Jingbo Zhu. Esrl: Efficient sampling-based reinforcement learning for sequence generation. InAAAI Confer- ence on Artificial Intelligence, 2...

  5. [20]

    URL https://api.semanticscholar.org/ CorpusID:282304438. Qwen An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxin Yang, Ji...

    arXiv

  6. [21]

    org/CorpusID:274859421

    URL https://api.semanticscholar. org/CorpusID:274859421. Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan. Webshop: Towards scalable real-world web interaction with grounded language agents.ArXiv, abs/2207.01206, 2022a. URL https://api.semanticscholar.org/CorpusID:25...

    arXiv

  7. [22]

    Yufeng Yuan, Yu Yue, Ruofei Zhu, Tiantian Fan, and Lin Yan

    URL https: //api.semanticscholar.org/CorpusID:288256157. 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, abs/2503.01491,

    arXiv

  8. [23]

    semanticscholar.org/CorpusID:276766648

    URL https://api. semanticscholar.org/CorpusID:276766648. Yu Yue, Yufeng Yuan, Qiying Yu, Xiaochen Zuo, Ruofei Zhu, Wenyuan Xu, Jiaze Chen, Chengyi Wang, Tiantian Fan, Zhengyin Du, Xiang Wei, Xiangyu Yu, Gaohong Liu, Juncai Liu, Lingjun Liu, Haibin Lin, Zhiqi Lin, Bole Ma, Chi ...

    arXiv

  9. [24]

    Xuhui Zhou, Hao Zhu, Leena Mathur, Ruohong Zhang, Haofei Yu, Zhengyang Qi, Louis philippe Morency, Yonatan Bisk, Daniel Fried, Graham Neubig, and Maarten Sap

    URL https://api.semanticscholar.org/CorpusID:277621526. Xuhui Zhou, Hao Zhu, Leena Mathur, Ruohong Zhang, Haofei Yu, Zhengyang Qi, Louis philippe Morency, Yonatan Bisk, Daniel Fried, Graham Neubig, and Maarten Sap. Sotopia: Interactive evaluation for social intelligence in lan...

    arXiv

  10. [1998]

    Hindsight credit assignment for long-horizon llm agents.ArXiv, abs/2603.08754,

    Huihan Tan, Xiaowen Yang, Hao Chen, Jiejing Shao, Yi Wen, Yuteng Shen, Weihong Luo, Xiku Du, Lan-Zhe Guo, and Yu-Feng Li. Hindsight credit assignment for long-horizon llm agents.ArXiv, abs/2603.08754,

  11. [2017]

    Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Jun-Mei Song, Mingchuan Zhang, Y

    URL https://api.semanticscholar.org/ CorpusID:28695052. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Jun-Mei Song, Mingchuan Zhang, Y . K. Li, Yu Wu, and Daya Guo. Deepseekmath: Pushing the limits of mathematical reasoning in open language models.ArXiv, abs/2402.03300,

    arXiv

  12. [2023]

    Izzeddin Gur, Hiroki Furuta, Austin Huang, Mustafa Safdari, Yutaka Matsuo, Douglas Eck, and Aleksandra Faust

    URL https://api.semanticscholar.org/CorpusID:258823350. Izzeddin Gur, Hiroki Furuta, Austin Huang, Mustafa Safdari, Yutaka Matsuo, Douglas Eck, and Aleksandra Faust. A real-world webagent with planning, long context understanding, and pro- gram synthesis.ArXiv, abs/2307.12856,

    arXiv

  13. [2024]

    URL https://api.semanticscholar.org/CorpusID: 272524129. Manling Li, Shiyu Zhao, Qineng Wang, Kangrui Wang, Yu Zhou, Sanjana Srivastava, Cem Gokmen, Tony Lee, Li Erran Li, Ruohan Zhang, Weiyu Liu, Percy Liang, Fei-Fei Li, Jiayuan Mao, and Jiajun Wu. Embodied agent interface: B...

    arXiv

  14. [2025]

    Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim, Suhong Moon, Hiroki Furuta, Gopala Krishna Anumanchipalli, Kurt Keutzer, and Amir Gholami

    URL https://api.semanticscholar.org/CorpusID:277065966. Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim, Suhong Moon, Hiroki Furuta, Gopala Krishna Anumanchipalli, Kurt Keutzer, and Amir Gholami. Plan-and-act: Improving planning of agents for long-horizon tasks.ArXiv, abs/2503.09572,

    arXiv

  15. [2026]

    URL https://api.semanticscholar.org/CorpusID:288672595. Karl Cobbe, Vineet Kosaraju, Mo Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word ...

    arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.