E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning

[2601.19969] E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning Skip to main content Search Submit Donate Log in Computer Science > Robotics arXiv:2601.19969 (cs) [Submitted on 27 Jan 2026 ( v1 ), last revised 25 Aug 2026 (this version, v2)] Title: E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning Authors: Haoyuan Deng , Yudong Lin , Yuanjiang Xue , Haoyang Du , Qianzhun Wang , Boyang Zhou , Zhenyu Wu , Ziwei Wang View PDF HTML (experimental) Abstract: Human-in-the-loop guidance has emerged as an effective approach for accelerating online reinforcement learning (RL) in real-world manipulation. However, existing human-in-the-loop RL (HiL-RL) frameworks often suffer from low sample efficiency, requiring substantial human interventions to achieve convergence and thereby leading to high labor costs. To address this, we propose a sample-efficient real-world human-in-the-loop RL framework named E2HiL, which requires fewer human interventions by actively selecting informative samples. Specifically, stable reduction of policy entropy enables improved trade-off between exploration and exploitation with higher sample efficiency. We first build influence functions of different samples on the policy entropy, which is efficiently estimated by the covariance of action probabilities and soft advantages of policies. Then we select samples with moderate values of influence functions, where shortcut samples that induce sharp entropy drops and noisy samples with negligible effect are pruned. Extensive experiments across 10 real-world manipulation tasks, spanning multiple embodiments and learning frameworks, demonstrate that E2HiL improves success rates by 24.9% while reducing human interventions by 9.3% compared to state-of-the-art HiL-RL baselines. These results validate its effectiveness as a policy- and embodiment-agnostic plug-and-play module for efficient real-world RL. The project page can be found at this https URL . Comments: Project page: this https URL Updated to the final IEEE RA-L version. The author list has been revised to match the published version, adding Yudong Lin and Qianzhun Wang. Main results and conclusions remain unchanged Subjects: Robotics (cs.RO) ; Machine Learning (cs.LG) Cite as: arXiv:2601.19969 [cs.RO] (or arXiv:2601.19969v2 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2601.19969 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE Robotics and Automation Letters, vol. 11, no. 7, pp. 8084-8091, 2026 Related DOI : https://doi.org/10.1109/LRA.2026.3693577 Focus to learn more DOI(s) linking to related resources Submission history From: Zhenyu Wu [ view email ] [v1] Tue, 27 Jan 2026 18:13:22 UTC (3,354 KB) [v2] Tue, 25 Aug 2026 07:09:28 UTC (4,744 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2026-01 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning#670473
• RPS: Information Elicitation with Reinforcement Prompt Selection#14045
• EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models#630018
• EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models#628156
• Official Review · ICLR 2026#787023