Attention as a Compass: Efficient Exploration for Process-Supervised RL
  in Reasoning Models
Authors: name: Runze Liu; name: Jiakang Wang; name: Yuling Shi; name: Zhihui Xie; name: Chenxin An; name: Kaiyan Zhang; name: Jian Zhao; name: Xiaodong Gu; name: Lei Lin; name: Wenping Hu; name: Xiu Li; name: Fuzheng Zhang; name: Guorui Zhou; name: Kun Gai
Published: 2025-09-30 17:58:34

Abstract: Reinforcement Learning (RL) has shown remarkable success in enhancing the
reasoning capabilities of Large Language Models (LLMs). Process-Supervised RL
(PSRL) has emerged as a more effective paradigm compared to outcome-based RL.
However, existing PSRL approaches suffer from limited exploration efficiency,
both in terms of branching positions and sampling. In this paper, we introduce
a novel PSRL framework (AttnRL), which enables efficient exploration for
reasoning models. Motivated by preliminary observations that steps exhibiting
high attention scores correlate with reasoning behaviors, we propose to branch
from positions with high values. Furthermore, we develop an adaptive sampling
strategy that accounts for problem difficulty and historical batch size,
ensuring that the whole training batch maintains non-zero advantage values. To
further improve sampling efficiency, we design a one-step off-policy training
pipeline for PSRL. Extensive experiments on multiple challenging mathematical
reasoning benchmarks demonstrate that our method consistently outperforms prior
approaches in terms of performance and sampling and training efficiency.