Unlocking Reasoning Capabilities in LLMs via Reinforcement Learning Exploration

• URL: http://arxiv.org/abs/2510.03865v2
• Date: Fri, 31 Oct 2025 06:08:26 GMT
• Title: Unlocking Reasoning Capabilities in LLMs via Reinforcement Learning Exploration
• Abstract summary: We propose RAPO, an algorithm to promote broader yet focused exploration.<n>We train Qwen2.5-3B and 7B models with RAPO on the 8K SimpleRL-Zero dataset.<n>Results show that RAPO consistently improves problem-solving performance.
• Score: 8.839121572048018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning with verifiable rewards (RLVR) has recently enhanced the reasoning capabilities of large language models (LLMs), particularly for mathematical problem solving. However, a fundamental limitation remains: as the sampling budget increases, the advantage of RLVR-trained models over their pretrained bases often diminishes or even vanishes, revealing a strong dependence on the base model's restricted search space. We attribute this phenomenon to the widespread use of the reverse Kullback-Leibler (KL) divergence regularizer, whose mode-seeking behavior keeps the policy trapped inside the base model's support region and hampers wider exploration. To address this issue, we propose RAPO (Rewards-Aware Policy Optimization), an algorithm to promote broader yet focused exploration. Our method (i) utilizes the forward KL penalty to replace the reverse KL penalty for out-of-distribution exploration, and (ii) reweights the reference policy to facilitate adaptive in-distribution exploration. We train Qwen2.5-3B and 7B models with RAPO on the 8K SimpleRL-Zero dataset, without supervised fine-tuning, and evaluate them on AIME2024 and AIME2025. Results show that RAPO consistently improves problem-solving performance. Notably, RAPO enables models to surpass the base model's performance ceiling and solves previously intractable problems, advancing the frontier of RLVR for challenging reasoning tasks.

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