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Bootstrapping Exploration with Group-Level Natural Language Feedback in Reinforcement Learning
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Bootstrapping Exploration with Group-Level Natural Language Feedback in Reinforcement Learning

#reinforcement learning #natural language feedback #bootstrapping #exploration #group-level #AI agents #efficiency

📌 Key Takeaways

  • Researchers propose using group-level natural language feedback to guide reinforcement learning agents.
  • This method helps agents explore environments more efficiently by leveraging human-like instructions.
  • The approach reduces the need for extensive trial-and-error by incorporating feedback at a collective level.
  • It demonstrates improved performance in complex tasks compared to traditional exploration strategies.

📖 Full Retelling

arXiv:2603.04597v1 Announce Type: cross Abstract: Large language models (LLMs) typically receive diverse natural language (NL) feedback through interaction with the environment. However, current reinforcement learning (RL) algorithms rely solely on scalar rewards, leaving the rich information in NL feedback underutilized and leading to inefficient exploration. In this work, we propose GOLF, an RL framework that explicitly exploits group-level language feedback to guide targeted exploration thro

🏷️ Themes

AI Exploration, Language Feedback

📚 Related People & Topics

Reinforcement learning

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In machine learning and optimal control, reinforcement learning (RL) is concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learnin...

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Entity Intersection Graph

Connections for Reinforcement learning:

🌐 Large language model 10 shared
🌐 Artificial intelligence 8 shared
🌐 Machine learning 4 shared
🏢 Science Publishing Group 2 shared
🌐 Reasoning model 2 shared
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Mentioned Entities

Reinforcement learning

Reinforcement learning

Field of machine learning

AI agent

Systems that perform tasks without human intervention
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Original Source
--> Computer Science > Computation and Language arXiv:2603.04597 [Submitted on 4 Mar 2026] Title: Bootstrapping Exploration with Group-Level Natural Language Feedback in Reinforcement Learning Authors: Lei Huang , Xiang Cheng , Chenxiao Zhao , Guobin Shen , Junjie Yang , Xiaocheng Feng , Yuxuan Gu , Xing Yu , Bing Qin View a PDF of the paper titled Bootstrapping Exploration with Group-Level Natural Language Feedback in Reinforcement Learning, by Lei Huang and 8 other authors View PDF Abstract: Large language models typically receive diverse natural language feedback through interaction with the environment. However, current reinforcement learning algorithms rely solely on scalar rewards, leaving the rich information in NL feedback underutilized and leading to inefficient exploration. In this work, we propose GOLF, an RL framework that explicitly exploits group-level language feedback to guide targeted exploration through actionable refinements. GOLF aggregates two complementary feedback sources: external critiques that pinpoint errors or propose targeted fixes, and intra-group attempts that supply alternative partial ideas and diverse failure patterns. These group-level feedbacks are aggregated to produce high-quality refinements, which are adaptively injected into training as off-policy scaffolds to provide targeted guidance in sparse-reward regions. Meanwhile, GOLF jointly optimizes generation and refinement within a unified RL loop, creating a virtuous cycle that continuously improves both capabilities. Experiments on both verifiable and non-verifiable benchmarks show that GOLF achieves superior performance and exploration efficiency, achieving 2.2$\times$ improvements in sample efficiency compared to RL methods trained solely on scalar rewards. Code is available at this https URL . Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04597 [cs.CL] (or arXiv:2603.04597v1 [cs.CL] for this version) https://doi.org/10.4855...
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