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Jin-Ning Li

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#artificial intelligence Preprint Sep 2026

Selecting Diverse SFT Traces Improves Post-RL Generalization

Verified solutions are not equally useful for preparing reasoning models for reinforcement learning (RL). We present a comprehensive study of route diversity, the variation in the sequences of reasoning steps in supervised fine-tuning (SFT) data, and propose a lightweight, rule-based fingerprint to select for it. From...

Dylan Zhang, Ming-Yuan Wu, Jin-Ning Li · 0 citations

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