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 Probing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned ModelsThe research shows that models fine-tuned with reinforcement learning develop more linearly separable and hierarchical representations compared to supervised fine-tuned models, revealing the internal mechanism by which this approach yields superior performance on mathematical reasoning tasks. This is a news summary compiled for the AiPulse newsletter — not the original article. The original article is at arxiv.org. Read the original article AI Critique → English (automatic translation) |