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cs.AI2026
Beyond the Mean: Multi-Moment Policy Optimization for LLM Reasoning
Yijun Zhang, Yule Xie, Jiaxin Ding +4
Reinforcement learning has become a central paradigm for improving the reasoning capabilities of large language models. Existing methods generally aim to reduce the failure probabi…
cs.AI2026
Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn Search Agents
Yijun Zhang, Fan Xu, Jiaxin Ding +6
Reinforcement learning (RL) improves large language model (LLM) agents on long-horizon search tasks that require multiple intermediate decisions before a final outcome. However, ro…
cs.AI2025
CATArena: Evaluating Evolutionary Capabilities of Code Agents via Iterative Tournaments
Lingyue Fu, Xin Ding, Linyue Pan +9
Current evaluation for Large Language Model (LLM) code agents predominantly focus on generating functional code in single-turn scenarios, which fails to evaluate the agent's capabi…