Self-play, a type of training algorithm that enables a model to self-improve, has recently shown promising empirical results in the context of formal theorem proving using Large Language Models (LLMs). (Dong & Ma, 2025) instantiate self-play with two cooperating agents: a prover, which proves theorems, and a conjecturer, which generates new theorems as a curriculum to the prover. In this paper, we provide a theoretical framework for understanding the self-improvement capabilities of self-play algorithms for theorem proving. First, we formalize the set of theorems as a graph, with nodes as theo
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Stanford NLP Group
@stanfordnlp
· 08/11 21:43
Natural Language Processing/Machine Learning @chrmanning @jurafsky @percyliang @ChrisGPotts @tatsu_hashimoto @MonicaSLam @Diyi_Yang @YejinChoinka @StanfordAILab
How can an LLM self-improve at theorem proving? Inspired by STP (Dong & Ma, 2025), we propose a theoretical framework for understanding prover-conjecturer self-play algorithms. NeurIPS 2026, Sydney Poster Session 2, Tue 5-8pm @ Hall 1-4. 1/8
Stanford AI Lab
@StanfordAILab
· 08/11 20:21
The Stanford Artificial Intelligence Laboratory (SAIL), a leading #AI lab since 1963. ⛵️🤖 Emmy-winning video: https://t.co/lV9smZTC1m
How can an LLM self-improve at theorem proving? Inspired by STP (Dong & Ma, 2025), we propose a theoretical framework for understanding prover-conjecturer self-play algorithms. NeurIPS 2026, Sydney Poster Session 2, Tue 5-8pm @ Hall 1-4. 1/8