Jul 2026
Probing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned Models
This work presents two converging lines of evidence that linear probes trained on layer-wise hidden states reveal that RL models tend to achieve higher accuracy in predicting answer correctness compared to SFT models, indicating more linearly separable and structured representations.
Antyabha Rahman, Akshaj Gurugubelli, Omar Ankit et al.
· arXiv.org · 0 citations