Show HN: TheoremSmith: Mass-Producing Hard Proof Problems to Train LLMs
Making models better at formal verification will help push the boundaries of math research and significantly reduce the number of bugs in our code. Recently, training models on RL environments (tasks paired with verifiers that reward correct solutions) has driven big gains in model performance, and proofs are unusually easy to verify. Here we introduce TheoremSmith, a pipeline for generating synthetic formal verification environments. It reliably creates multi-hour, frontier-difficulty tasks and
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points · 4 votes
2026-11-10 19:15:20.659Z
cumulative since publication
研究分析
变化记录
来源描述：Making models better at formal verification will help push the boundaries of math research and significantly reduce the number of bugs in our code. Recently, training models on RL environments (tasks paired with verifiers that reward correct solutions) has driven big gains in model performance, and proofs are unusually easy to verify. Here we introduce TheoremSmith, a pipeline for generating synthetic formal verification environments. It reliably creates multi-hour, frontier-difficulty tasks and
版本与成本证据
来源票数: 4 votes (2026年11月10日 UTC; cumulative since publication) 可供比较的近期同类记录：“Show HN: A sanctions and PEP screening API” (Show HN, 2026年11月10日)；“Show HN: You can now train your own LLMs with less memory, on your consumer GPUs” (Hacker News, 2026年11月10日)。主题相近仅用于导航，未核验其是否属于同一需求或独立证据。
适用与受影响对象
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缺失的比较信息
待补齐：新旧行为、受影响套餐与地区、生效时间、兼容性、回退路径及同一工作量下的成本。
采用与迁移验证
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