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 through actionable refinements. GOLF aggregates two complementary feedback sources: (i) external critiques that pinpoint errors or propose targeted fixes, and (ii) 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 https://github.com/LuckyyySTA/GOLF.
Bootstrapping Code Translation with Weighted Multilanguage Exploration
State Key Laboratory for Novel Software Technology, Nanjing University, China（南京大学新型软件技术国家重点实验室）；National Institute of Healthcare Data Science, Nanjing University, China（南京大学医疗数据科学国家研究院）
Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning
VU Amsterdam（阿姆斯特丹自由大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Leiden University（莱顿大学）；University of Warsaw（华沙大学）；Simon Fraser University（西蒙菲莎大学）；The University of Hong Kong（香港大学）
Towards Efficient Online Exploration for Reinforcement Learning with Human Feedback
Department of Statistics and Data Science, The Chinese University of Hong Kong（统计与数据科学系，香港中文大学）；Department of Statistics, University of Wisconsin-Madison（统计系，威斯康星大学麦迪逊分校）
Socratic-Zero : Bootstrapping Reasoning via Data-Free Agent Co-evolution
Alibaba Group Holding Limited（阿里巴巴集团控股有限公司）；EPIC Lab, Shanghai Jiao Tong University（上海交通大学EPIC实验室）；Shanghai University of Finance and Economics（上海财经大学）；Wuhan University（武汉大学）；Zhejiang University（浙江大学）
SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Mutual Enhancement of Large Language and Reinforcement Learning Models through Bi-Directional Feedback Mechanisms: A Planning Case Study
Technical University of Munich（慕尼黑工业大学）；Peking University（北京大学）；BIGAI（北京智源人工智能研究院）；King's College London（伦敦国王学院）；Tongji University（同济大学）；University College London（伦敦大学学院）
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
The University of Liverpool（利物浦大学）；Northwestern University（西北大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；National Taiwan University（国立台湾大学）；Nanyang Technological University（南洋理工大学）；The Swiss AI Lab IDSIA/USI/SUPSI（瑞士IDSIA人工智能实验室（隶属于瑞士意大利语区大学与瑞士应用科学大学））；The University of Southampton（南安普顿大学）
SaFeR-Steer: Evolving Multi-Turn MLLMs via Synthetic Bootstrapping and Feedback Dynamics
Huazhong University of Science and Technology（华中科技大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；West China Biomedical Big Data Center, Sichuan University（四川大学西部生物医学大数据中心）；School of Public Policy and Administration, Chongqing University（重庆大学公共政策与管理学院）；Nanyang Technological University（南洋理工大学）
