most citedLearn the Ropes, Then Trust the Wins: Self-imitation with Progressive Exploration for Agentic Reinforcement Learning

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cs.LG2026

Start Classifying: Categorical Critics for LLM Reinforcement Learning

Zhijian Zhou, Long Li, Xuan Zhang +7

Proximal Policy Optimization (PPO) for large language models typically trains its critic by mean-squared-error (MSE) regression on scalar value targets. Although scalar MSE is stat…

cs.LG2025★ 1 cited

Learn the Ropes, Then Trust the Wins: Self-imitation with Progressive Exploration for Agentic Reinforcement Learning

Yulei Qin, Xiaoyu Tan, Zhengbao He +13

Reinforcement learning (RL) is the dominant paradigm for sharpening strategic tool use capabilities of LLMs on long-horizon, sparsely-rewarded agent tasks, yet it faces a fundament…