most citedCoEvolve: Training LLM Agents via Agent-Data Mutual Evolution

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cs.LG2026

APPO: Agentic Procedural Policy Optimization

Xucong Wang, Ziyu Ma, Yong Wang +5

Recent advances in agentic Reinforcement Learning (RL) have substantially improved the multi-turn tool-use capabilities of large language model agents. However, most existing metho…

cs.LG2026

DEvo: Dual Difficulty-Aware Self-Evolution for Data-Efficient Reinforcement Learning

Ru Zhang, Renda Li, Ziyu Ma +4

Reinforcement learning (RL) has demonstrated potential for enhancing reasoning in large language models (LLMs). However, effective RL training, which requires medium-difficulty tra…

cs.LG2025

Tree Search for LLM Agent Reinforcement Learning

Yuxiang Ji, Ziyu Ma, Yong Wang +3

Recent advances in reinforcement learning (RL) have significantly enhanced the agentic capabilities of large language models (LLMs). In long-term and multi-turn agent tasks, existi…