Distilling LLM Feedback for Lean Theorem Proving
Improving formal reasoning through token-level feedback and self-distillation.
Post-training for reasoning models typically combines supervised fine-tuning with reinforcement learning from verifiable rewards, most commonly with GRPO. However, this algorithm suffers from sparse rewards, limited exploration, and mode collapse. Building upon recent works on se...
ArXiv CS.AI
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Feedback Distillation addresses the critical failure modes of reinforcement learning in theorem proving by providing dense, token-level supervision instead of sparse reward signals. This approach bridges the gap between raw exploration and structured reasoning, significantly enhancing the reliability of formal mathematical verification.
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