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cs.LG2026
EVPO: Explained Variance Policy Optimization for Adaptive Critic Utilization in LLM Post-Training
Chengjun Pan, Shichun Liu, Jiahang Lin +10
Reinforcement learning (RL) for LLM post-training faces a fundamental design choice: whether to use a learned critic as a baseline for policy optimization. Classical theory favors…
cs.LG2025
AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning
Zhiheng Xi, Jixuan Huang, Chenyang Liao +20
Developing autonomous LLM agents capable of making a series of intelligent decisions to solve complex, real-world tasks is a fast-evolving frontier. Like human cognitive developmen…
cs.LG2025
Improving RL Exploration for LLM Reasoning through Retrospective Replay
Shihan Dou, Muling Wu, Jingwen Xu +4
Reinforcement learning (RL) has increasingly become a pivotal technique in the post-training of large language models (LLMs). The effective exploration of the output space is essen…