Learning Lean Feedback for Efficient Proof-Candidate Evaluation on GPUs

Key points

• Slow checks and timeouts often leave GPUs waiting and withhold rewards from potentially correct proofs.1
• We train neural evaluators to predict Lean 4 verdicts and compiler feedback on GPUs using 4.84M labelled programs.1
• We compare two validity classifiers with a feedback model that annotates proof attempts with predicted goal states and error messages.1
• After one GRPO epoch with hybrid rewards, the prover achieves nearly the same miniF2F pass@1 as with Lean-only rewards (62.2% versus 62.4%) and a pass@32 of 72.4% versus 71.3%.1

Sources (1)

• [1]Learning Lean Feedback for Efficient Proof-Candidate Evaluation on GPUs
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 01:46 PM

  Training language models for formal theorem proving requires large numbers of verifier calls.

  Slow checks and timeouts often leave GPUs waiting and withhold rewards from potentially correct proofs.

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