KEPO: Knowledge-Enhanced Preference Optimization for Reinforcement Learning with Reasoning

• URL: http://arxiv.org/abs/2602.00400v1
• Date: Fri, 30 Jan 2026 23:28:37 GMT
• Title: KEPO: Knowledge-Enhanced Preference Optimization for Reinforcement Learning with Reasoning
• Authors: Fan Yang, Rui Meng, Trudi Di Qi, Ali Ezzati, Yuxin Wen,
• Abstract summary: Reinforcement learning has emerged as a promising paradigm for inducing explicit reasoning behaviors in large language and vision-language models.<n>However, reasoning-oriented RL post-training remains fundamentally challenging due to sparse trajectory-level rewards.<n>Recent on-policy distillation methods introduce dense teacher supervision to stabilize optimization, but apply it uniformly across all generated trajectories.
• Score: 24.072603982041798
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL) has emerged as a promising paradigm for inducing explicit reasoning behaviors in large language and vision-language models. However, reasoning-oriented RL post-training remains fundamentally challenging due to sparse trajectory-level rewards, leading to ambiguous credit assignment and severe exploration failures that can trap the policy in a ``learning cliff.'' Recent on-policy distillation methods introduce dense teacher supervision to stabilize optimization, but apply it uniformly across all generated trajectories. We argue that such uniform distillation is ill-suited for reasoning-intensive tasks, as low-quality on-policy trajectories often originate from early logical errors, and distillation under flawed contexts injects noisy and misaligned gradients. To address these challenges, we propose Knowledge-Enhanced Preference Optimization (KEPO), a unified post-training framework that integrates: (i) a quality-gated on-policy distillation objective that selectively applies dense teacher guidance only to high-quality trajectories, and (ii) a knowledge-enhanced exploration strategy that leverages hints learned from a teacher model to rejectively sample reward-positive on-policy trajectories for RL, thereby mitigating exploration collapse. Evaluated on a challenging medical visual question answering benchmark under single-source generalization, KEPO demonstrates improved training stability, more coherent reasoning behaviors, and superior out-of-distribution performance over reinforcement learning and on-policy distillation baselines.

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