Reasoning-Aware GRPO using Process Mining

TaekHyunPark, Lee, Hyerim Bae

Reasoning-Aware GRPO using Process Mining: 42 upvotes on Hugging Face Daily Papers, #7 of 30 papers on 2025-10-30. Day-by-day upvote history.

Reinforcement learning (RL)-based post-training has been crucial for enabling multi-step reasoning in large reasoning models (LRMs), yet current reward schemes are typically outcome-centric. We propose PM4GRPO, a reasoning-aware Group Relative Policy Optimization (GRPO) that augments standard answer/format rewards with signals over the reasoning procedure. To this end, process mining techniques are utilized to compute a scalar conformance reward that measures how closely a policy model's reasoning aligns with the pretrained teacher model. The empirical results on five benchmarks demonstrate that PM4GRPO significantly outperforms existing methodologies for GRPO-based post-training. These results highlight that leveraging process mining for reasoning-aware GRPO effectively enhances the reasoning capabilities of policy models.

Paper page on Hugging Face · arXiv

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