On the Mechanism of Reasoning Pattern Selection in Reinforcement Learning for Language Models

• URL: http://arxiv.org/abs/2506.04695v1
• Date: Thu, 05 Jun 2025 07:17:04 GMT
• Title: On the Mechanism of Reasoning Pattern Selection in Reinforcement Learning for Language Models
• Authors: Xingwu Chen, Tianle Li, Difan Zou,
• Abstract summary: We present a systematic study of Reinforcement Learning with Verifiable Rewards (RLVR)<n>We show that RLVR-trained models preferentially adopt high-success-rate reasoning patterns.<n>We develop theoretical analyses on the convergence and training dynamics of RLVR.
• Score: 17.36077163968198
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL) has demonstrated remarkable success in enhancing model capabilities, including instruction-following, preference learning, and reasoning. Yet despite its empirical successes, the mechanisms by which RL improves reasoning abilities remain poorly understood. We present a systematic study of Reinforcement Learning with Verifiable Rewards (RLVR), showing that its primary benefit comes from optimizing the selection of existing reasoning patterns. Through extensive experiments, we demonstrate that RLVR-trained models preferentially adopt high-success-rate reasoning patterns while mostly maintaining stable performance on individual patterns. We further develop theoretical analyses on the convergence and training dynamics of RLVR based on a simplified question-reason-answer model. We study the gradient flow and show that RLVR can indeed find the solution that selects the reason pattern with the highest success rate. Besides, our theoretical results reveal two distinct regimes regarding the convergence of RLVR training: (1) rapid convergence for models with relatively strong initial reasoning capabilities versus (2) slower optimization dynamics for weaker models. Furthermore, we show that the slower optimization for weaker models can be mitigated by applying the supervised fine-tuning (SFT) before RLVR, when using a feasibly high-quality SFT dataset. We validate the theoretical findings through extensive experiments. This work advances our theoretical understanding of RL's role in LLM fine-tuning and offers insights for further enhancing reasoning capabilities.

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