REVIEW 4 major objections 6 minor 1 cited by
Filtering Learning Histories Enhances In-Context Reinforcement Learning
T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read Filtering learning histories by an improvement-and-stability score before pretraining consistently improves in-context reinforcement learning results for AD, DICP, and DPT, with larger gains when the data are noisy.
desk verdict A useful first dataset-filtering method for ICRL, with honest but under-powered empirical support; the specific metric is not isolated, so publish with controls. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central carrying object is the unified score $U(D^i_l) = \mathrm{Improvement}(D^i_l) + \lambda\,\mathrm{Stability}(D^i_l)$, where Improvement combines the mean episodic return with the max-minus-min return gap (scaled by the environment's maximal return) and Stability measures the absence of performance drops via the mean of negative successive return differences. A normalized version of $U$ becomes the retention probability in a random sampling step, so that histories with higher improvement and stability are kept more often and the pretraining dataset size stays fixed. This converts the standard ERM pretraining objective into a weighted variant, which the paper argues transfers the WERM benefit to ICRL.
What would settle it
Train AD on Darkroom twice: once with LHF filtering by $U$ as defined in the paper, and once with the inverse ranking (keeping the lowest-$U$ histories); the claim predicts a large performance gap favoring LHF, while a null result—equal in-context returns—would show the metric does not rank histories by training value.
Extended reading notes
Core claim
The central discovery is that transformer-based ICRL models need not imitate entire learning histories to learn good in-context behavior: pruning histories that show little improvement or unstable episodic returns before pretraining yields better zero-shot improvement on unseen environments. Concretely, LHF defines, for each learning history, an improvement score based on the mean episodic return and the return gap, and a stability score based on the mean of successive negative return differences, combining them into a unified metric with a coefficient $\lambda$. Histories are then sampled with probability proportional to a normalized version of this metric, emulating weighted empirical risk minimization. The paper reports consistent relative performance gains over unfiltered baselines across the AD, DICP, and DPT backbones, with the largest improvements appearing when the dataset is noisy (e.g., 27.8% average relative gain for AD with 30% random agents) and in difficult continuous tasks such as a 141% relative gain for DICP on Reach-Wall.
Load-bearing premise
The load-bearing premise is that the paper's hand-defined scores for improvement and stability correctly rank which learning histories are most useful for teaching in-context behavior; if that ranking is wrong, the filter would remove exactly the histories that teach exploration or recovery, and the method could harm rather than help.
Editorial extensions
If this is right
- LHF is a plug-in preprocessing step: any ICRL algorithm that trains on learning histories can adopt it without changing its architecture, loss function, or test-time procedure.
- When the pretraining dataset is noisy (e.g., 30% random agents), the average relative gains of LHF grow from single digits to 27.8% for AD, so the value of filtering increases as the source data become less reliable.
- LHF remains effective when only the first half of each learning history is available and when the transformer model is halved in size, indicating the benefit does not depend on full histories or large models.
- The paper reports gains with both PPO and SAC as source algorithms, with average relative improvements of 44.0% for AD and 9.2% for DICP on the SAC-collected tasks, indicating the effect is not tied to one data source.
- In the hardest continuous task, DICP with LHF reaches a 141% relative improvement over the unfiltered baseline on Reach-Wall, a signal that the method can have a large impact on suboptimal data.
Reading between the lines
- If the ranking signal truly captures training value, a natural extension is to learn it: a small scoring model could predict which histories to keep, potentially outperforming the fixed linear formula on new domains, though the paper does not test this.
- The stability term, being based on negative return differences, may interact with sparse-reward environments; in such settings an alternative like TD-error or advantage-weighted scores might rank histories differently, which is a testable hypothesis beyond the paper's scope.
- The filtering operates at the level of whole histories; a per-segment filter (e.g., dropping late stale episodes within a kept history) could further concentrate model capacity, but this is an extension the paper does not address.
- Because LHF only reweights data already collected, its gains can be combined with other data-centric interventions such as noise distillation or trust-horizon trimming, which the paper does not demonstrate but its mechanism would permit.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes Learning History Filtering (LHF), a pretraining-dataset preprocessing method for in-context reinforcement learning (ICRL). LHF computes two per-history metrics, Improvement (Eq. 6) and Stability (Eq. 7), combines them linearly via a coefficient lambda into a unified score U (Eq. 8), and then resamples learning histories per environment with probability proportional to a min-max normalized version of U (Eq. 9), using sampling with replacement until each environment's original dataset size is restored. The authors evaluate LHF with three ICRL backbones (AD, DPT, DICP) on four Darkroom-type discrete tasks and, for AD and DICP, on eight Meta-World-ML1 continuous manipulation tasks. They report average relative enhancements of 8.8%, 9.1%, and 11.9% over the unfiltered baselines on the discrete tasks, with larger gains on noisy datasets and in several suboptimal scenarios. Sensitivity analyses cover the stability coefficient lambda, softmax sampling with varying temperature, partial learning histories, lightweight transformer models, and SAC-collected data.
Significance. If the claimed effect is real, LHF is a simple, algorithm-agnostic, and plug-and-play contribution that could improve any ICRL pipeline without modifying the backbone, while also opening a data-centric direction for mitigating source suboptimality. The paper's empirical scope is broad: three backbones, two benchmark families, and multiple robustness scenarios, with detailed hyperparameters and a fixed lambda (chosen a priori) that avoids circular parameter fitting on test performance. These strengths are substantial. However, the central attribution of the gains to the specific improvement/stability ranking is not yet established, because the paper lacks control ablations that separate this mechanism from generic resampling or from simply up-weighting high-return histories. In addition, the headline relative-enhancement tables report no confidence intervals or significance tests, making it difficult to assess the small per-task effects. The absence of released code further limits verification. Overall, the work is promising but requires additional evidence to support the mechanism-specific claim and the consistency of the enhancements.
major comments (4)
- [§4, Algorithm 1 and Eq. (9)] The paper's central claim is that improvement and stability characteristics drive the enhancement, but there is no ablation controlling for the resampling-with-replacement scheme or comparing against alternative quality scores. LHF should be compared to (a) a control that resamples uniformly (constant P) to the same dataset size, (b) a control using a different per-history score such as final episodic return or mean return alone, and (c) a control that uses the same linear/softmax scoring but with a shuffled or random U. Without such ablations, the observed gains could be attributed to the duplication of high-return histories (a distributional shift independent of the metric) rather than to the specific "improvement and stability" characterization that defines LHF.
- [§5.3, Tables 1–3] The relative-enhancement tables report single numbers with no confidence intervals, standard errors, or significance tests, despite the learning curves showing only three independent runs with visible overlap between solid and dashed curves. Many per-task enhancements are small (e.g., 1.8% for AD on Dark Key-to-Door, 3.5% for DPT on Darkroom, 2.4% for DICP on Button-Press) and could easily be within run-to-run noise. The paper should provide error bars or paired significance tests on the relative enhancement E, state how E is aggregated over the three runs, and report per-run variability so that the claim of consistent improvement can be assessed.
- [§5.3 and Appendix B, Tables 3, 6, 7, 8] Several algorithm–task combinations show negative relative enhancements (e.g., AD on Push -0.4%, DICP on Hand-Insert -0.8%, DPT on Darkroom-Permuted -2.8% in Table 6 and -1.3% in Table 7, DICP on Soccer -0.6% in Table 8). The text mentions these only in passing ("except for the case of ..."). Given the paper's claim of consistent enhancement, these counterexamples deserve a more transparent discussion of when LHF can hurt and why practitioners should still adopt it. At minimum, report these as negative results rather than averaging them into the headline numbers, and consider whether the conclusion "consistently outperforms" is warranted.
- [§4, Eq. (9)] The per-environment min-max normalization forces the best history in every environment to be retained with probability 1, even if that history is poor in absolute terms. Consequently, LHF cannot remove environments whose entire set of histories is suboptimal; it only removes relative within-environment inferiority. This limitation directly affects the stated goal of "avoiding source suboptimality" (Abstract and Section 1) and should be discussed explicitly, or the normalization should be modified so that histories are evaluated against a global or task-level reference rather than a per-environment min-max range.
minor comments (6)
- [§6] The word "aross" in the first paragraph should be "across".
- [§5.4] In the sentence "highlighting the significance of balancing the improvement and stability during the ICRL pertaining", "pertaining" should be "pretraining".
- [§5.4, Figure 5] The figure captions show "AD-LHF( =0)" with a missing parameter symbol; this is likely a rendering issue and should be fixed to "AD-LHF(λ=0)" and "AD-LHF(α=...)" respectively.
- [§4] The sentence "Notice that for any set of weights in (4) between 0 and 1, it is always possible to define a probability distribution P̄w such that (5) becomes equivalent to (4)" is imprecise; the equivalence requires that the weight be absorbed into a normalized joint distribution over (τ, C, sq, al), which is not automatic when weights are history-level rather than transition-level.
- [§5.3] The phrase "All positive relative enhancement in Table 1" should read "All relative enhancements in Table 1 are positive" for grammatical correctness.
- [Appendix D] The statement "The codes will be made available upon the publication of this work" places a significant barrier to reproducibility; a public code release with the submitted version would strengthen the paper.
Circularity Check
No significant circularity: LHF's filtering is an independent preprocessing heuristic, not a fitted prediction or a self-citation reduction.
full rationale
The paper's central claim is that reweighting ICRL pretraining histories by the hand-crafted metric U = Improvement + lambda*Stability improves downstream in-context RL. This is an empirical preprocessing intervention, not a derivation whose output is equivalent to its input. The metric in Eqs. (6)-(8) is defined directly from source learning histories and the environment maximum return; it is never fit to test returns, and lambda is fixed a priori at lambda=1 with a sensitivity analysis, so no fitted parameter is renamed as a prediction. The improvement/stability scores are not defined in terms of the test-time in-context return that LHF is claimed to enhance, so there is no self-definitional collapse. The only self-citation is [27] (Chen and Paternain), used alongside [25,26] to support the standard premise that ICRL is a supervised pretraining procedure sensitive to the pretraining dataset; this premise is also established by the external AD/DPT baselines and is not load-bearing by itself. No uniqueness theorem or ansatz is imported from the authors' prior work. Section 6 explicitly leaves theoretical characterization as open, which is a limitation rather than a circular move. The absence of a random-filtering control is an experimental-validity concern, but it does not make any prediction equivalent to its inputs by construction.
Assumptions & free parameters
free parameters (2)
- lambda (stability coefficient) =
1 (fixed across main experiments; varied in Section 5.4)
- alpha (Softmax temperature) =
varied over {0.0625, 0.125, 0.25, 0.5, 1} in sensitivity analysis
assumptions (3)
- ad hoc to paper Improvement and stability metrics in Eqs. (6)-(7) capture the learning-history qualities that matter for ICRL pretraining.
- domain assumption Maximum episodic return Rmax per environment is known.
- domain assumption Weighted sampling guided by U emulates WERM and improves ICRL pretraining.
Cite this review
Pith. "Pith review of Filtering Learning Histories Enhances In-Context Reinforcement Learning." pith.science (2026). https://pith.science/paper/H6J6PSTM
@misc{pith2026250515143,
author = {Pith},
title = {Pith review of: Filtering Learning Histories Enhances In-Context Reinforcement Learning},
year = {2026},
howpublished = {\url{https://pith.science/paper/H6J6PSTM}},
note = {Machine review of arXiv:2505.15143}
}
read the original abstract
Transformer models (TMs) have exhibited remarkable in-context reinforcement learning (ICRL) capabilities, allowing them to generalize to and improve in previously unseen environments without re-training or fine-tuning. This is typically accomplished by imitating the complete learning histories of a source RL algorithm over a substantial amount of pretraining environments, which, however, may transfer suboptimal behaviors inherited from the source algorithm/dataset. Therefore, in this work, we address the issue of inheriting suboptimality from the perspective of dataset preprocessing. Motivated by the success of the weighted empirical risk minimization, we propose a simple yet effective approach, learning history filtering (LHF), to enhance ICRL by reweighting and filtering the learning histories based on their improvement and stability characteristics. To the best of our knowledge, LHF is the first approach to avoid source suboptimality by dataset preprocessing, and can be combined with the current state-of-the-art (SOTA) ICRL algorithms. We substantiate the effectiveness of LHF through a series of experiments conducted on the well-known ICRL benchmarks, encompassing both discrete environments and continuous robotic manipulation tasks, with three SOTA ICRL algorithms (AD, DPT, DICP) as the backbones. LHF exhibits robust performance across a variety of suboptimal scenarios, as well as under varying hyperparameters and sampling strategies. Notably, the superior performance of LHF becomes more pronounced in the presence of noisy data, indicating the significance of filtering learning histories.
Figures
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Forward citations
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Reference graph
Works this paper leans on
-
[1]
Playing atari with deep reinforcement learning
V olodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013
arXiv 2013
-
[2]
Human-level control through deep reinforcement learning
V olodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. nature, 518(7540):529–533, 2015
2015
-
[3]
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel. End-to-end training of deep visuomotor policies. The Journal of Machine Learning Research, 17(1):1334–1373, 2016
2016
-
[4]
Benchmarking deep reinforcement learning for continuous control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel. Benchmarking deep reinforcement learning for continuous control. In International conference on machine learning, pages 1329–1338. PMLR, 2016
2016
-
[5]
Reinforcement learning based recommender systems: A survey
M Mehdi Afsar, Trafford Crump, and Behrouz Far. Reinforcement learning based recommender systems: A survey. ACM Computing Surveys, 55(7):1–38, 2022
2022
-
[6]
A survey on reinforcement learning for recommender systems
Yuanguo Lin, Yong Liu, Fan Lin, Lixin Zou, Pengcheng Wu, Wenhua Zeng, Huanhuan Chen, and Chunyan Miao. A survey on reinforcement learning for recommender systems. IEEE Transactions on Neural Networks and Learning Systems, 2023
work page 2023
-
[7]
Reinforcement learning: An introduction
Richard S Sutton. Reinforcement learning: An introduction. A Bradford Book, 2018
2018
-
[8]
A survey of meta-reinforcement learning
Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa Zintgraf, Chelsea Finn, and Shimon Whiteson. A survey of meta-reinforcement learning. arXiv preprint arXiv:2301.08028, 2023
arXiv 2023
Show all 37 references
-
[9]
A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. A survey on in-context learning. arXiv preprint arXiv:2301.00234, 2022
2022 arXiv
-
[10]
Trans- formers as algorithms: Generalization and stability in in-context learning
Yingcong Li, Muhammed Emrullah Ildiz, Dimitris Papailiopoulos, and Samet Oymak. Trans- formers as algorithms: Generalization and stability in in-context learning. In International Conference on Machine Learning, pages 19565–19594. PMLR, 2023
2023
-
[11]
Larger language models do in-context learning differently
Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, et al. Larger language models do in-context learning differently. arXiv preprint arXiv:2303.03846, 2023
2023 arXiv
-
[12]
The learnability of in-context learning
Noam Wies, Yoav Levine, and Amnon Shashua. The learnability of in-context learning. Advances in Neural Information Processing Systems, 36, 2024
2024
-
[13]
In-context reinforcement learning with algorithm distillation
Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steiger- wald, DJ Strouse, Steven Hansen, Angelos Filos, Ethan Brooks, et al. In-context reinforcement learning with algorithm distillation. arXiv preprint arXiv:2210.14215, 2022
-
[14]
Transformers as decision makers: Provable in-context reinforcement learning via supervised pretraining
Licong Lin, Yu Bai, and Song Mei. Transformers as decision makers: Provable in-context reinforcement learning via supervised pretraining. arXiv preprint arXiv:2310.08566, 2023
2023 arXiv
-
[15]
In-context reinforcement learning for variable action spaces
Viacheslav Sinii, Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman, and Sergey Kolesnikov. In-context reinforcement learning for variable action spaces. arXiv preprint arXiv:2312.13327, 2023
2023 arXiv
-
[16]
Emergence of in-context reinforcement learning from noise distillation
Ilya Zisman, Vladislav Kurenkov, Alexander Nikulin, Viacheslav Sinii, and Sergey Kolesnikov. Emergence of in-context reinforcement learning from noise distillation. arXiv preprint arXiv:2312.12275, 2023. 10
2023 arXiv
-
[17]
Supervised pretraining can learn in-context reinforcement learning
Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, and Emma Brunskill. Supervised pretraining can learn in-context reinforcement learning. Advances in Neural Information Processing Systems, 36, 2024
2024
-
[18]
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017
2017 arXiv
-
[19]
Distilling reinforcement learning algorithms for in-context model-based planning
Jaehyeon Son, Soochan Lee, and Gunhee Kim. Distilling reinforcement learning algorithms for in-context model-based planning. In The Thirteenth International Conference on Learning Representations, 2025
2025
-
[20]
Attention is all you need
A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017
2017
-
[21]
Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch. Decision transformer: Reinforcement learning via sequence modeling. Advances in neural information processing systems, 34:15084–15097, 2021
2021
-
[22]
Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine. Offline reinforcement learning as one big sequence modeling problem. Advances in neural information processing systems, 34:1273– 1286, 2021
2021
-
[23]
Multi-game decision transformers
Kuang-Huei Lee, Ofir Nachum, Mengjiao Sherry Yang, Lisa Lee, Daniel Freeman, Sergio Guadarrama, Ian Fischer, Winnie Xu, Eric Jang, Henryk Michalewski, et al. Multi-game decision transformers. Advances in Neural Information Processing Systems, 35:27921–27936, 2022
2022
-
[24]
A generalist agent
Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al. A generalist agent. arXiv preprint arXiv:2205.06175, 2022
2022 arXiv
-
[25]
Emergence of in-context reinforcement learning from noise distillation
Ilya Zisman, Vladislav Kurenkov, Alexander Nikulin, Viacheslav Sinii, and Sergey Kolesnikov. Emergence of in-context reinforcement learning from noise distillation. In Forty-first Interna- tional Conference on Machine Learning, 2024
2024
-
[26]
In-context reinforcement learning without optimal action labels
Juncheng Dong, Moyang Guo, Ethan X Fang, Zhuoran Yang, and Vahid Tarokh. In-context reinforcement learning without optimal action labels. In ICML 2024 Workshop on In-Context Learning, 2024
2024
-
[27]
Random policy enables in-context reinforcement learning within trust horizons
Weiqin Chen and Santiago Paternain. Random policy enables in-context reinforcement learning within trust horizons. Transactions on Machine Learning Research, 2025. Featured Certification
2025
-
[28]
Local rademacher complexities
Peter L Bartlett, Olivier Bousquet, and Shahar Mendelson. Local rademacher complexities. The Annals of Statistics, 2005
2005
-
[29]
Risk bounds for statistical learning
Pascal Massart and Élodie Nédélec. Risk bounds for statistical learning. The Annals of Statistics, 2006
2006
-
[30]
Reweighting improves conditional risk bounds
Yikai Zhang, Jiahe Lin, Fengpei Li, Songzhu Zheng, Anant Raj, Anderson Schneider, and Yuriy Nevmyvaka. Reweighting improves conditional risk bounds. Transactions on Machine Learning Research, 2025
2025
-
[31]
Hierarchical reinforcement learning for zero- shot generalization with subtask dependencies
Sungryull Sohn, Junhyuk Oh, and Honglak Lee. Hierarchical reinforcement learning for zero- shot generalization with subtask dependencies. Advances in neural information processing systems, 31, 2018
2018
-
[32]
Improving zero-shot generalization in offline reinforcement learning using generalized similarity functions.Advances in Neural Information Processing Systems, 35:25088–25101, 2022
Bogdan Mazoure, Ilya Kostrikov, Ofir Nachum, and Jonathan J Tompson. Improving zero-shot generalization in offline reinforcement learning using generalized similarity functions.Advances in Neural Information Processing Systems, 35:25088–25101, 2022
2022
-
[33]
Explore to generalize in zero-shot rl
Ev Zisselman, Itai Lavie, Daniel Soudry, and Aviv Tamar. Explore to generalize in zero-shot rl. Advances in Neural Information Processing Systems, 36:63174–63196, 2023. 11
2023
-
[34]
A survey of zero-shot generalisation in deep reinforcement learning
Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel. A survey of zero-shot generalisation in deep reinforcement learning. Journal of Artificial Intelligence Research , 76:201–264, 2023
2023
-
[35]
Tinyllama: An open-source small language model
Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, and Wei Lu. Tinyllama: An open-source small language model. arXiv preprint arXiv:2401.02385, 2024
2024 arXiv
-
[36]
Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al. Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905, 2018
2018 arXiv
-
[37]
Gridworld
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine. Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning. In Conference on robot learning, pages 1094–1100. PMLR, 2020. 12 A Implementation and Ex...
2020
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