First Return, Entropy-Eliciting Explore
Authors: name: Tianyu Zheng, name: Tianshun Xing, name: Qingshui Gu, name: Taoran Liang, name: Xingwei Qu, name: Xin Zhou, name: Yizhi Li, name: Zhoufutu Wen, name: Chenghua Lin, name: Wenhao Huang, name: Qian Liu, name: Ge Zhang, name: Zejun Ma

Reinforcement Learning from Verifiable Rewards (RLVR) improves the reasoning
abilities of Large Language Models (LLMs) but it struggles with unstable
exploration. We propose FR3E (First Return, Entropy-Eliciting Explore), a
structured exploration framework that identifies high-uncertainty decision
points in reasoning trajectories and performs targeted rollouts to construct
semantically grounded intermediate feedback. Our method provides targeted
guidance without relying on dense supervision. Empirical results on
mathematical reasoning benchmarks(AIME24) show that FR3E promotes more stable
training, produces longer and more coherent responses, and increases the
proportion of fully correct trajectories. These results highlight the
framework's effectiveness in improving LLM reasoning through more robust and
structured exploration.