5 papers
Progressive Agent Skill Generation via Reinforcement Learning
Junhao Shen, Zhanqiu Zhang, Yiwen Guo +1
Existing skill generation methods largely rely on heuristics or pipeline-style consolidation, which must be specially designed for different evidence sources. In contrast, learning…
Dynamic Skill Lifecycle Management for Agentic Reinforcement Learning
Junhao Shen, Teng Zhang, Xiaoyan Zhao +1
Large language model agents increasingly rely on external skills to solve complex tasks, where skills act as modular units that extend their capabilities beyond what parametric mem…
Achieving Olympia-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning
Haiteng Zhao, Junhao Shen, Yiming Zhang +7
Large language model (LLM) agents exhibit strong mathematical problem-solving abilities and can even solve International Mathematical Olympiad (IMO) level problems with the assista…
Intern-S1-MO: Long-horizon Reasoning Agent for Olympiad?Level Mathematical Problem Solving
Songyang Gao, Yuzhe Gu, Zijian Wu +18
Large Reasoning Models (LRMs) have expanded the mathematical reasoning frontier through Chain-of-Thought (CoT) techniques and Reinforcement Learning with Verifiable Rewards (RLVR),…
Semi-off-Policy Reinforcement Learning for Vision-Language Slow-Thinking Reasoning
Junhao Shen, Haiteng Zhao, Yuzhe Gu +7
Enhancing large vision-language models (LVLMs) with visual slow-thinking reasoning is crucial for solving complex multimodal tasks. However, since LVLMs are mainly trained with vis…