Probing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned Models

arXiv:2607.26119v2 Announce Type: replace Abstract: Large language models trained with reinforcement learning (RL) to reason step by step have been increasingly shown to outperform their supervised fine-tuned (SFT) counterparts on mathematical reasoning tasks. Yet the mechanistic basis for this improvement remains unclear. We therefore ask, what internal representational differences enable RL-trained reasoning language models to reason better? Our work presents two primary lines of evidence: First, layer-wise linear probes tend to predict answer correctness more accurately in RL-trained reason

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