DeepInsightTheorem teaches models proof insight

This is a useful reframing of math reasoning: not just longer chains of thought, but better supervision over the strategic move that makes a proof work.

• –The dataset builds on DeepTheorem’s 121K informal theorem-proof pairs and restructures examples into technique, sketch, and full-proof stages.
• –The training curriculum moves from direct proof generation to sketch-conditioned generation to technique-guided reasoning, which is a cleaner recipe than simply dumping richer traces into SFT.
• –The strongest practical signal is small-model improvement; the paper argues insight-guided structure helps raise the ceiling for 1.5B- to 8B-class models.
• –Evaluation still depends on LLM judges, so the claims are promising but should be read as proof-quality scoring, not formal verification.
• –For developers working on reasoning systems, the takeaway is that data shape may matter as much as model scale or RL when the task requires finding the right conceptual move.