Feb. 9, 2024, 5:44 a.m. | Amitayush Thakur George Tsoukalas Yeming Wen Jimmy Xin Swarat Chaudhuri

cs.LG updates on arXiv.org arxiv.org

We present an in-context learning agent for formal theorem-proving in environments like Lean and Coq. Current state-of-the-art models for the problem are finetuned on environment-specific proof data. By contrast, our approach, called COPRA, repeatedly asks a high-capacity, general-purpose large language model (GPT-4) to propose tactic applications from within a stateful backtracking search. Proposed tactics are executed in the underlying proof environment. Feedback from the execution is used to build the prompt for the next model query, along with selected information …

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