Pith. sign in

REVIEW 4 major objections 6 minor 34 references

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read The paper claims that sample-efficiency gains in reinforcement learning come from coordinating components, not from stacking them, and presents ROSER as a coordinated framework that beats naive stacking by 17.60%.

desk verdict A useful component-interaction study in continuous control, but the flagship 17.60% number cannot be reproduced from the paper's own tables and the same-suite design-to-evaluation path weakens the framework claim. read the letter →

arxiv 2608.07086 v1 pith:7HVG5466 submitted 2026-08-07 cs.LG cs.AI

Qi Zhao , Guozheng Ma , Yilun Kong , Lu Li , Haoyu Wang , Zilin Wang , Tiantian Zhang , Yuxing Wang
show 4 more authors
Jian Sha Yongzhe Chang Xueqian Wang Dacheng Tao
This is my paper · ORCID
classification cs.LGcs.AI
keywords reinforcementlearningsampleefficiencycomponentsynergycontinuouscontrolexperiencereplayoptimizationstabilitymodel-basedrepresentationsoftactor-critic
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper asks whether independently successful sample-efficiency tricks in reinforcement learning help or hurt when combined. It reports that naive stacking of a representation learner, a stability-oriented network architecture, and prioritized replay often degrades performance, and that the degradation is an emergent coordination problem rather than a flaw of any single module. To address it, the paper proposes ROSER, a framework that pairs a SimBa-style stable backbone with a residual-style information bypass and a delayed prioritization schedule. Across 18 continuous-control tasks, ROSER consistently beats vanilla SAC and its partial variants, and improves on naive stacking by 17.60% in aggregate. The work's central thesis is that sample efficiency is a systems property: gains come from coordinating modules, not from isolating them.

What carries the argument

The load-bearing mechanism is ROSER's three-part coordination recipe. First, the SimBa residual-block architecture with layer normalization serves as the optimization-stability backbone for actor, critic, and encoder. Second, R* concatenates the raw state and action with the learned latent embeddings before they enter the policy and value networks, giving downstream heads a residual-style bypass to stable input signals. Third, U2P schedules the replay-prioritization exponent $\alpha_t$, keeping $\alpha = 0$ until $t_{\text{start}} = 0.2$ of training, interpolating to $\alpha_F = 0.4$ between $t_{\text{start}}$ and $t_{\text{end}} = 0.8$, and holding it afterward; it also switches the critic loss from MSE to Huber once prioritization begins, following the loss-sampling equivalence.

What would settle it

Run ROSER with the U2P schedule disabled or shifted (for example, $\alpha_F = 0.0$, or $t_{\text{start}} = 0.0$) on a held-out set of continuous-control tasks that were not used in the paper's diagnostic section; if the performance gap over naive stacking collapses on those tasks, the claim that principled coordination generalizes is not supported.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that sample-efficiency components interact in regime-dependent ways: optimization stability acts as a foundational enabler, model-based representation only helps once stable information flow is supplied via bypass connections, and prioritized replay actively hurts an already-stabilized system unless prioritization is introduced gradually. The paper demonstrates this through controlled ablations on the same 18-task suite, culminating in ROSER, which combines SimBa, R*, and U2P and reports a significant Pareto improvement over vanilla SAC and its augmented variants. The analysis further shows that R* and U2P can look neutral or harmful in isolation on a vanilla SAC baseline, yet become clearly beneficial inside the full ROSER system, supporting the claim that their value is coordination-driven rather than standalone.

Load-bearing premise

The paper develops its two fixes, R* and U2P, on the same 18 tasks it later uses to prove the gains, so the central claim depends on the assumption that the fixes generalize beyond those development benchmarks rather than overfit to them.

Editorial extensions

If this is right

  • If coordination rather than accumulation drives sample efficiency, future RL systems should be evaluated in integrated settings rather than as isolated module upgrades on a vanilla baseline.
  • Optimization stability can be treated as a prerequisite: a stable backbone unlocks gains from representation learning that do not appear on an unstable base.
  • Residual-style bypasses between learned features and downstream heads may serve as a general design pattern beyond the specific R* instantiation.
  • Delaying the onset of prioritized replay until learning signals stabilize is a concrete lever for avoiding interference between replay and representation learning.
  • Because ROSER is described as algorithm-agnostic and is also tested on DDPG, the coordination recipe can transfer to other off-policy continuous-control learners.

Reading between the lines

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

  • If the diagnosis is right, then the same interference pattern should appear with other representation learners or replay priorities: a testable prediction is that substituting a different self-supervised encoder or a different priority rule would recreate the need for a bypass and a schedule.
  • The hand-set U2P schedule values are an untested degree of freedom; varying $\alpha_F$, $t_{\text{start}}$, or $t_{\text{end}}$ on held-out tasks would reveal how much of the gain depends on the particular numbers rather than on the principle of gradual prioritization.
  • A natural extension is to treat the three principles as a checklist for adding further components, such as exploration bonuses, where compounded non-stationarity is likely to reappear in a new form.
  • The aggregate 17.60% figure may disguise per-task variance; per-task results in the paper show the gain is concentrated in harder locomotion and manipulation regimes, so the headline should be read as an aggregate, not a guarantee on every task.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper investigates whether three RL sample-efficiency components (model-based representation, optimization stability, and experience replay) interact synergistically or interfere when combined on top of SAC in continuous control. It reports that naive stacking often triggers interference, distills three design principles from this diagnosis, and instantiates them in ROSER, which combines a SimBa backbone, a residual-style representation bypass (R*), and a scheduled prioritized replay scheme (U2P). The empirical evaluation compares ROSER with vanilla SAC, single-component variants, and naive stacking across 18 tasks from DMC, HumanoidBench, MyoSuite, and ManiSkill2, using IQM with bootstrap confidence intervals, and the abstract claims a 17.60% gain over naive stacking.

Significance. If substantiated, the finding that individually successful sample-efficiency components can interfere in integrated systems, and that coordinated design (stable backbone, stable information flow, delayed prioritization) recovers and improves sample efficiency, is a valuable contribution to RL system design. The paper's strengths include 8-seed runs on 18 tasks, bootstrap confidence intervals, performance profiles, an isolated-versus-synergy ablation for R* and U2P, and a DDPG generalization check in the appendix. However, the headline quantitative claim is not reproducible from the reported tables, and the two novel components were designed using the same benchmark suite on which they are validated, with no sensitivity analysis or held-out tasks. The central insight is plausible, but these issues need to be addressed before the specific quantitative claims can be accepted.

major comments (4)
  1. [Abstract, §5.1, Table 12] The abstract and introduction claim that ROSER 'achieves 17.60% gains over naive stack,' but Table 12 (Appendix F) reports IQM 310 [293, 329] for Naive Stack and 374 [357, 390] for ROSER at 500k steps, which is a 20.65% improvement; the mean gives 13.37% and the median gives 5.34%. None of these values equals 17.60%. The paper must state the exact aggregation rule and evaluation point used for the headline number, or correct the number; as written, a reader cannot verify the central quantitative claim from the provided data.
  2. [§3.3, Eq. (3), Table 6] R* and U2P are introduced in Section 3 after diagnosing failures on the same 18-task suite used in Section 5 to validate ROSER. In particular, the U2P schedule parameters (alpha_F=0.4, t_start=0.2, t_end=0.8) are hand-chosen repairs with no sensitivity analysis and no held-out tasks. This makes the claim that these are general coordination principles vulnerable to benchmark overfitting. I recommend either splitting tasks into development and validation sets, or providing a sensitivity sweep over the schedule parameters, to demonstrate that the design choices are not tuned to the evaluation suite.
  3. [Table 1, §5.2] The evidence that U2P helps inside ROSER is weak: for ROSER with ER(P) versus ER(U2P), the IQM is 0.671 [0.604, 0.723] versus 0.713 [0.671, 0.748], and the intervals overlap substantially. This comparison uses only 5 seeds on 6 tasks. The text's claim that U2P's 'primary value lies in buffer-level regularization' goes beyond what these data support; please add more seeds or soften the conclusion.
  4. [§3.1–§3.3, Figures 2, 4, 5] The diagnostic takeaways that motivate the design principles are based on figures without numerical aggregates or confidence intervals; for example, §3.2 reports a 24.72% locomotion boost for R* over +R without a supporting table. Since these comparisons are the evidence for the principles, provide IQM values with bootstrap CIs for the specific configurations shown in Figures 2, 4, and 5.
minor comments (6)
  1. [§3, Experiment Setup] There is a typo: 'we focuse on tasks' should be 'we focus on tasks'.
  2. [§5.1, Figure 6] The phrase 'success density' is vague; please define it precisely or replace it with a standard term such as 'probability of exceeding threshold'.
  3. [Table 12 caption] The caption says aggregate mean, median, and IQM are computed over the default reward, but tasks use different metrics (return vs. success rate); clarify how scores are normalized before aggregation.
  4. [Table 6, §3.3] The switch from MSE loss to Huber loss when alpha>0 is noted in Table 6, but it is not explained in the main text; discuss this design choice in §3.3.
  5. [Eq. (3)] The sampling probability is written as P(i) = p_i^alpha / sum_k p_k^alpha, but p_i is not defined in the main text; define p_i (e.g., the ReLo priority) for completeness.
  6. [References] Reference [29] has a formatting typo: 'arXiv preprint arXiv: Arxiv-2208.10483' should be 'arXiv preprint arXiv:2208.10483'.

Circularity Check

2 steps flagged · score 4.0 of 10

Design fixes (R⋆, U2P) are selected on the same 18-task suite later used to report the 17.60% gain.

  1. fitted input called prediction [Section 3.2 (R⋆ design) → Section 5.1 evaluation]
    "We empirically evaluate the necessity of this design by comparing SAC+OS+R against its stabilized counterpart, SAC+OS+R⋆. As shown in Figure 4, the R⋆ variant significantly outperforms the naive combination... The task configurations and environments used here are consistent with those employed in Section 3."

    The R⋆ residual-bypass is introduced in Section 3.2 after observing that 'SAC+OS+R' (naive R with SimBa) underperforms; it is the proposed fix. Section 5.1 then evaluates ROSER on 'task configurations and environments used here are consistent with those employed in Section 3'—the same 18 tasks used to select R⋆. The improvement attributed to R⋆ (24.72%/11.51% in Figure 4) and the final '17.60% over naive stack' are therefore measured on the development set, not on held-out tasks; the design is fitted to the benchmark, so the prediction is partly by construction.

  2. fitted input called prediction [Section 3.3 (Eq. 3, Table 6) → Section 5.1 evaluation]
    "Based on this consideration, we propose Uniform-to-Prioritized replay (U2P). ... As illustrated in Figure 5, incorporating U2P successfully mitigates the conflict between ER with R⋆ and OS. ... The task configurations and environments used here are consistent with those employed in Section 3."

    The U2P schedule (Eq. 3) is introduced in Section 3.3 specifically because ER(P) 'underperforms SAC+OS+R⋆' on the same 18-task suite; the parameter values α_F=0.4, t_start=0.2, t_end=0.8 in Table 6 are chosen from that observation. Section 5.1 then reports ROSER's gains on 'task configurations and environments used here are consistent with those employed in Section 3'—the identical benchmark. Thus the U2P contribution and the headline gain are in-sample evaluations of a hand-fitted schedule. The paper's Limitations admit R⋆ and U2P are 'primarily empirically-grounded.'

full rationale

ROSER's central contribution is an empirical coordination recipe, and most of its inputs (SimBa, MR.Q, ReLo) are external and correctly cited; the self-citations [17, 18, 22] are not load-bearing. The main circularity is the design-evaluation loop: Section 3 uses the 18-task suite both to diagnose failures of naive stacking and to motivate the two novel fixes (R⋆ residual bypass and U2P schedule), and Section 5.1 explicitly reuses 'the task configurations and environments used here are consistent with those employed in Section 3' to validate ROSER. The U2P hyperparameters (α_F=0.4, t_start=0.2, t_end=0.8) are hand-chosen on that same suite, and the abstract's '17.60% gains' is reported from that same evaluation. This is not a mathematical equivalence, but it is a fitted-input-called-prediction pattern: the fixes are selected using the benchmark that later supplies the evidence. The paper's own Limitations section concedes R⋆ and U2P are 'primarily empirically-grounded.' Some independent content exists—the DDPG transfer (Appendix A) shows algorithm-level generality, and the qualitative interference finding is a real empirical observation—so the circularity is partial. Separately, the abstract's 17.60% number does not match any aggregate in Table 12 (IQM gives 20.65%, mean 13.37%, median 5.34%); that is a verifiability issue, not a circularity, and lowers confidence but does not change the circularity score.

Assumptions & free parameters 3 free parameters · 3 assumptions · 2 invented entities

The central empirical claims rest on a handful of hand-chosen schedule parameters, on the representativeness of the 18-task benchmark suite, and on the transferability of hyperparameters from prior component papers. R* and U2P are algorithmic constructs introduced for this paper, and they lack independent evidence outside the same benchmark suite.

free parameters (3)
  • U2P priority ceiling alpha_F = 0.4
    Hand-chosen in Eq. 3; controls the degree of non-uniform sampling at the end of the schedule. No tuning procedure or sensitivity analysis is reported.
  • U2P schedule start t_start = 0.2 * total env steps
    Hand-chosen start of the prioritization ramp in Eq. 3; no sensitivity analysis is reported.
  • U2P schedule end t_end = 0.8 * total env steps
    Hand-chosen end of the prioritization ramp in Eq. 3; no sensitivity analysis is reported.
assumptions (3)
  • domain assumption The 18 selected tasks from DMC, HumanoidBench, MyoSuite, and ManiSkill2 are representative of continuous-control RL and are not chosen to favor ROSER.
    The paper says it focuses on tasks that remain challenging, and the same suite is used both to motivate the design choices and to evaluate them.
  • domain assumption The original hyperparameters of MR.Q, SimBa, and ReLo remain appropriate when integrated into a new framework.
    The paper adopts these components with their original hyperparameters and assumes they transfer without re-tuning.
  • domain assumption SAC is a suitable base algorithm for studying component synergy and conclusions extend to other off-policy algorithms.
    Generalization to DDPG is tested on only 8 tasks, so the broader claim about algorithm-agnostic design is only weakly supported.
invented entities (2)
  • R* residual-style feature bypass
    purpose: Concatenates raw state and action with learned latent embeddings before the actor and critic to stabilize information flow.
    Introduced after observing that naive +R integration degrades performance on the evaluation suite; no independent benchmark or theory supports it outside this paper.
  • U2P scheduled prioritized replay
    purpose: Delays and gradually increases priority sampling to avoid early-stage sampling bias in integrated systems.
    Schedule parameters are hand-chosen and validated only on the same tasks used to motivate the design.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control." pith.science (2026). https://pith.science/paper/7HVG5466

@misc{pith2026260807086,
  author       = {Pith},
  title        = {Pith review of: Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7HVG5466}},
  note         = {Machine review of arXiv:2608.07086}
}
read the original abstract

Reinforcement learning systems are significantly more complex than other machine learning paradigms due to inherent properties, causing RL system design to jointly account for many tightly coupled factors. Despite advances in individual algorithmic components, their functional interdependencies remain underexplored: do they exhibit mutual synergy or counterproductive interference? To bridge this gap, we conduct a systematic investigation and find that the efficacy of different components exhibits significant task-dependency, and naively stacking state-of-the-art techniques does not necessarily yield performance gains; instead, it often triggers emergent challenges, such as compounded non-stationarity. Building upon these findings, we distill a suite of actionable insights into the principled coordination of these components. Guided by these insights, we propose ROSER, an RL framework that coordinates three critical dimensions: Model-based Representation, Optimization Stability, and Experience Replay. Across diverse continuous-control benchmarks, ROSER consistently outperforms vanilla baselines and achieves 17.60% gains over naive stack. Our findings underscore the necessity of a holistic perspective in RL system design and paves the way for developing sample-efficient agents.

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

34 extracted references · 23 canonical work pages

  1. [1]

    Deep reinforcement learning at the edge of the statistical precipice.Advances in Neural Information Processing Systems, 2021

    Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, and Marc G Bellemare. Deep reinforcement learning at the edge of the statistical precipice.Advances in Neural Information Processing Systems, 2021. 5, 9

    work page 2021

  2. [2]

    PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation

    Jason Ansel, Edward Yang, Horace He, Natalia Gimelshein, Animesh Jain, Michael Voznesensky, Bin Bao, Peter Bell, David Berard, Evgeni Burovski, Geeta Chauhan, Anjali Chourdia, Will Constable, Alban Desmaison, Zachary DeVito, Elias Ellison, Will Feng, Jiong Gong, Michael Gschwind, Brian Hirsh, Sherlock Huang, Kshiteej Kalambarkar, Laurent Kirsch, Michael L...

    arXiv 2024

  3. [3]

    Representation learning: A review and new perspectives, 2014

    Yoshua Bengio, Aaron Courville, and Pascal Vincent. Representation learning: A review and new perspectives, 2014. URLhttps://arxiv.org/abs/1206.5538. 3

    arXiv 2014

  4. [4]

    Revisitingrainbow: Promotingmoreinsightful andinclusivedeepreinforcementlearningresearch

    JohanSamirObandoCeronandPabloSamuelCastro. Revisitingrainbow: Promotingmoreinsightful andinclusivedeepreinforcementlearningresearch. InInternationalConferenceonMachineLearning, pages 1373–1383. PMLR, 2021. 3

    work page 2021

  5. [5]

    Beyond the rainbow: High perfor- mance deep reinforcement learning on a desktop PC

    Tyler Clark, Mark Towers, Christine Evers, and Jonathon Hare. Beyond the rainbow: High perfor- mance deep reinforcement learning on a desktop PC. InForty-second International Conference on Machine Learning, 2025. URLhttps://openreview.net/forum?id=V3KXsUFw8D. 3

    work page 2025

  6. [6]

    Revisiting fundamentals of experience replay, 2020

    William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, and Will Dabney. Revisiting fundamentals of experience replay, 2020. URLhttps: //arxiv.org/abs/2007.06700. 4

    arXiv 2020

  7. [7]

    An equivalence between loss functions and non-uniform sampling in experience replay.Advances in Neural Information Processing Systems, 33,

    Scott Fujimoto, David Meger, and Doina Precup. An equivalence between loss functions and non-uniform sampling in experience replay.Advances in Neural Information Processing Systems, 33,

    work page

  8. [8]

    Smith, Shixiang Shane Gu, Doina Precup, and David Meger

    Scott Fujimoto, Wei-Di Chang, Edward J. Smith, Shixiang Shane Gu, Doina Precup, and David Meger. For SALE: State-action representation learning for deep reinforcement learning. InThirty-seventh Conference on Neural Information Processing Systems, 2023. URLhttps://openreview.net/ forum?id=xZvGrzRq17. 1

    work page 2023

Show all 34 references
  1. [9]

    Towards general- purpose model-free reinforcement learning

    Scott Fujimoto, Pierluca D’Oro, Amy Zhang, Yuandong Tian, and Michael Rabbat. Towards general- purpose model-free reinforcement learning. InThe Thirteenth International Conference on Learning Representations, 2025. URLhttps://openreview.net/forum?id=R1hIXdST22. 1, 3, 18, 21

    2025

  2. [10]

    Jiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling, Xiqiaing Liu, Tongzhou Mu, Yihe Tang, Stone Tao, Xinyue Wei, Yunchao Yao, Xiaodi Yuan, Pengwei Xie, Zhiao Huang, Rui Chen, and Hao Su. 12 Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient C...

    2023

  3. [11]

    World models

    David Ha and Jürgen Schmidhuber. World models. 2018. doi: 10.5281/ZENODO.1207631. URL https://zenodo.org/record/1207631. 3

    2018

  4. [12]

    Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

    Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. InInternational conference on machine learning, pages 1861–1870. Pmlr, 2018. 5

    2018

  5. [13]

    Dream to control: Learning behaviors by latent imagination

    Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination. InInternational Conference on Learning Representations, 2020. URLhttps://openreview.net/forum?id=S1lOTC4tDS. 3

    2020

  6. [14]

    Td-mpc2: Scalable,robustworldmodelsforcontinuous control

    NicklasHansen,HaoSu,andXiaolongWang. Td-mpc2: Scalable,robustworldmodelsforcontinuous control. InInternational Conference on Learning Representations (ICLR), 2024. 17

    2024

  7. [15]

    Rainbow: Combining improvements in deep reinforcement learning

    Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver. Rainbow: Combining improvements in deep reinforcement learning. InProceedings of the AAAI conference on artificial intelligence, ...

  8. [16]

    Plasticity loss in deep reinforcement learning: A survey, 2024

    Timo Klein, Lukas Miklautz, Kevin Sidak, Claudia Plant, and Sebastian Tschiatschek. Plasticity loss in deep reinforcement learning: A survey, 2024. URLhttps://arxiv.org/abs/2411.04832. 4

    2024 arXiv

  9. [17]

    Mastering massive multi-task reinforcement learning via mixture-of-expert decision transformer

    Yilun Kong, Guozheng Ma, Qi Zhao, Haoyu Wang, Li Shen, Xueqian Wang, and Dacheng Tao. Mastering massive multi-task reinforcement learning via mixture-of-expert decision transformer. In ICLR 2025 Workshop on Modularity for Collaborative, Decentralized, and Continual Deep Learning,

    2025

  10. [18]

    QPO: Query-dependent prompt optimization via multi-loop offline reinforcement learning.Transactions on Machine Learning Research, 2025

    Yilun Kong, Hangyu Mao, Zhao Qi, Bin Zhang, Jingqing Ruan, Li Shen, Yongzhe Chang, Xueqian Wang, Rui Zhao, and Dacheng Tao. QPO: Query-dependent prompt optimization via multi-loop offline reinforcement learning.Transactions on Machine Learning Research, 2025. ISSN 2835-8856. U...

    2025

  11. [19]

    Large batch experience replay, 2021

    Thibault Lahire, Matthieu Geist, and Emmanuel Rachelson. Large batch experience replay, 2021. 1, 4, 5, 24

    2021

  12. [20]

    Wurman, Jaegul Choo, Peter Stone, and Takuma Seno

    Hojoon Lee, Dongyoon Hwang, Donghu Kim, Hyunseung Kim, Jun Jet Tai, Kaushik Subramanian, Peter R. Wurman, Jaegul Choo, Peter Stone, and Takuma Seno. Simba: Simplicity bias for scaling up parameters in deep reinforcement learning. InThe Thirteenth International Conference on Le...

    2025

  13. [21]

    Hyperspherical normalization for scalable deep reinforcement learning

    HojoonLee,YoungdoLee,TakumaSeno,DonghuKim,PeterStone,andJaegulChoo. Hyperspherical normalization for scalable deep reinforcement learning. InForty-second International Conference on Machine Learning, 2025. URLhttps://openreview.net/forum?id=kfYxyvCYQ4. 1 13 Beyond Isolation: U...

    2025

  14. [22]

    Rethinking the role of dynamic sparse training for scalable deep reinforcement learning, 2025

    Guozheng Ma, Lu Li, Zilin Wang, Haoyu Wang, Shengchao Hu, Leszek Rutkowski, and Dacheng Tao. Rethinking the role of dynamic sparse training for scalable deep reinforcement learning, 2025. URLhttps://arxiv.org/abs/2510.12096. 4

    2025

  15. [23]

    Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, and Pulkit Agrawal

    Srinath V. Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, and Pulkit Agrawal. Random latent exploration for deep reinforcement learning. InForty-first International Conference on Machine Learning, 2024. URLhttps://openreview.net/forum?id=Y9qzwNlKVU. 1

    2024

  16. [24]

    Bigger, regularized, optimistic: scaling for compute and sample-efficient continuous control

    Michal Nauman, Mateusz Ostaszewski, Krzysztof Jankowski, Piotr Miłoś, and Marek Cygan. Bigger, regularized, optimistic: scaling for compute and sample-efficient continuous control. InAdvances in Neural Information Processing Systems, 2024. URLhttps://arxiv.org/pdf/2405.16158. 1, 4

    2024 arXiv

  17. [25]

    The primacy bias in deep reinforcement learning

    Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro, Pierre-Luc Bacon, and Aaron Courville. The primacy bias in deep reinforcement learning. InInternational Conference on Machine Learning. PMLR, 2022. 4

    2022

  18. [26]

    Prioritized experience replay.arXiv preprint arXiv:1511.05952, 2015

    Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. Prioritized experience replay.arXiv preprint arXiv:1511.05952, 2015. 1, 4, 5, 24

    2015 arXiv

  19. [27]

    Humanoid- bench: Simulated humanoid benchmark for whole-body locomotion and manipulation.arXiv Preprint arxiv:2403.10506, 2024

    Carmelo Sferrazza, Dun-Ming Huang, Xingyu Lin, Youngwoon Lee, and Pieter Abbeel. Humanoid- bench: Simulated humanoid benchmark for whole-body locomotion and manipulation.arXiv Preprint arxiv:2403.10506, 2024. 5, 23

    2024 arXiv

  20. [28]

    Curl: Contrastive unsupervised representations for reinforcement learning, 2020

    Aravind Srinivas, Michael Laskin, and Pieter Abbeel. Curl: Contrastive unsupervised representations for reinforcement learning, 2020. URLhttps://arxiv.org/abs/2004.04136. 3

    2020 arXiv

  21. [29]

    Shivakanth Sujit, Somjit Nath, Pedro H. M. Braga, and Samira Ebrahimi Kahou. Prioritizing samples in reinforcement learning with reducible loss.arXiv preprint arXiv: Arxiv-2208.10483, 2022. 4, 5, 19, 24

    2022 arXiv

  22. [30]

    MaxinfoRL: Boosting exploration in reinforcement learning through information gain maximization

    Bhavya Sukhija, Stelian Coros, Andreas Krause, Pieter Abbeel, and Carmelo Sferrazza. MaxinfoRL: Boosting exploration in reinforcement learning through information gain maximization. InThe ThirteenthInternationalConferenceonLearningRepresentations, 2025. URL https://openreview....

    2025

  23. [31]

    Deepmind control suite.arXiv preprint arXiv:1801.00690, 2018

    Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al. Deepmind control suite.arXiv preprint arXiv:1801.00690, 2018. 5, 23

    2018 arXiv

  24. [32]

    Myosuite – a contact-rich simulation suite for musculoskeletal motor control.https://github

    Caggiano Vittorio, Wang Huawei, Durandau Guillaume, Sartori Massimo, and Kumar Vikash. Myosuite – a contact-rich simulation suite for musculoskeletal motor control.https://github. com/myohub/myosuite, 2022. URLhttps://arxiv.org/abs/2205.13600. 5, 23

    2022 arXiv

  25. [33]

    Towards sample efficient reinforcement learning

    Yang Yu. Towards sample efficient reinforcement learning. InIJCAI, pages 5739–5743, 2018. 1 14 Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Appendix AROSERon DDPG 16 B Hyperparameters 17 C Architecture 19 C.1 MLP ...

    2018

  26. [2025]

    URLhttps://openreview.net/forum?id=YgR8U5DSj9. 1

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.