Enhancing LLMs with the ability to actively search external knowledge is crucial for complex and real-world tasks. Current approaches either rely on prompting to elicit the model's innate agent capabilities, or suffer from performance ceilings and collapse when applying RL to complex interactive tasks, leaving their true agentic potential untapped. To address this, we introduce \textbf{D}ynamic-filter \textbf{S}equence-level \textbf{P}olicy \textbf{O}ptimization (DSPO), an improved RL algorithm designed for robust agent training through sequence-level optimization and dynamic sample filtering. We train our model purely through RL to interleave multi-turn search and reasoning, obviating the need for supervised demonstration data. Across multiple QA benchmarks, our 7B model improves over a comparable previous work by \textbf{34.1\%}, and even outperforms the 14B model from previous work in complex multihop QA such as HotpotQA by nearly \textbf{9\% relative}, maintaining exceptional training stability.
SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning
Tsinghua University（清华大学）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）；Nanyang Technological University（南洋理工大学）；Tongji University（同济大学）
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；The Chinese University of Hong Kong Shenzhen（香港中文大学（深圳））；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
SkillScaffold: Scaffolding Agentic Reinforcement Learning with Policy-Adaptive Skill Credit Assignment and Selective Injection
Zhejiang University（浙江大学）；Kuaishou Technology（快手科技）；University of the Chinese Academy of Sciences（中国科学院大学）；Zhejiang University of Science and Technology（浙江科技学院）
EAVer: Long-Form Factuality Verification as an End-to-End Agentic Policy
University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Taobao & Tmall Group（淘宝天猫集团）；Shandong University（山东大学）；Peking University（北京大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
EDGE: Experience-Distillation for Guided Exploration in Agentic Reinforcement Learning
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉科学学院）；Wuhan AI Research（武汉人工智能研究院）
RSIPO: Recursive Self-Improving Policy Optimization for Agentic Reinforcement Learning via Skill-Policy Co-Evolution
THU（清华大学）；NUS（新加坡国立大学）；Tongji University（同济大学）；UW-Madison（威斯康星大学麦迪逊分校）；Westlake University（西湖大学）；CUHK（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；PKU（北京大学）
GAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in Knowledge Base Question Answering
University of Science and Technology of China（中国科学技术大学）；NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；ShanghaiTech University（上海科技大学）；Ant Group（蚂蚁集团）
OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning
Tsinghua University（清华大学）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）；Nanyang Technological University（南洋理工大学）；Tongji University（同济大学）
SSE-Bio: A Structured Self-Evolving Agent with Agentic Retrieval Policy for Multi-Hop Biomedical Reasoning
School of Computing Science, University of Glasgow（格拉斯哥大学计算科学学院）；Brigham and Women’s Hospital, Harvard Medical School, Harvard University（哈佛大学医学院附属布莱根妇女医院）；Language Technology Lab, University of Cambridge（剑桥大学语言技术实验室）；School of Natural and Computing Science, University of Aberdeen（阿伯丁大学自然与计算科学学院）；School of Cancer Sciences, University of Glasgow（格拉斯哥大学癌症科学学院）；Cancer Research UK Scotland Institute（英国癌症研究苏格兰研究所）
Look Ahead Before You Distill: Future Trajectory Validation of Teacher Guidance for Agentic On-Policy Distillation
Meituan LongCat Interaction（美团龙猫交互）；Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；Peking University（北京大学）；Nanjing University（南京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Jilin University（吉林大学）；University of Science and Technology of China（中国科学技术大学）；The Hong Kong Polytechnic University（香港理工大学）
ACE-GraphRAG: Agentic Context Engineering for Hierarchical GraphRAG
The Chinese University of Hong Kong（香港中文大学）；Wuhan University of Technology（武汉理工大学）；The Hong Kong University of Science and Technology（香港科技大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
