Process-Verified Reinforcement Learning for Theorem Proving via Lean
📰 ArXiv cs.AI
Learn how to apply process-verified reinforcement learning to theorem proving using Lean, a symbolic proof assistant, to achieve dense and sound feedback
Action Steps
- Implement a reinforcement learning framework using Lean as a symbolic process oracle
- Define a reward function that incorporates the fine-grained structured feedback from Lean
- Train a reinforcement learning agent to optimize the reward function and generate proofs
- Verify the correctness of the generated proofs using Lean's formal reasoning capabilities
- Compare the performance of the process-verified reinforcement learning approach with traditional RLVR methods
Who Needs to Know This
Researchers and developers in AI and formal reasoning can benefit from this work, as it provides a new approach to reinforcement learning for theorem proving
Key Insight
💡 Using a symbolic proof assistant like Lean as a process oracle can provide rich, fine-grained feedback for reinforcement learning in theorem proving
Share This
💡 Process-verified reinforcement learning for theorem proving via Lean: dense & sound feedback for AI-powered formal reasoning
Key Takeaways
Learn how to apply process-verified reinforcement learning to theorem proving using Lean, a symbolic proof assistant, to achieve dense and sound feedback
Full Article
Title: Process-Verified Reinforcement Learning for Theorem Proving via Lean
Abstract:
arXiv:2606.20068v1 Announce Type: new Abstract: While reinforcement learning from verifiable rewards (RLVR) typically has relied on a single binary verification signal, symbolic proof assistants in formal reasoning offer rich, fine-grained structured feedback. This gap between structured processes and unstructured rewards highlights the importance of feedback that is both dense and sound. In this work, we demonstrate that the Lean proof assistant itself can serve as a symbolic process oracle, su
Abstract:
arXiv:2606.20068v1 Announce Type: new Abstract: While reinforcement learning from verifiable rewards (RLVR) typically has relied on a single binary verification signal, symbolic proof assistants in formal reasoning offer rich, fine-grained structured feedback. This gap between structured processes and unstructured rewards highlights the importance of feedback that is both dense and sound. In this work, we demonstrate that the Lean proof assistant itself can serve as a symbolic process oracle, su
DeepCamp AI