czg1225/VeriThinker
[NeurIPS 2025] VeriThinker: Learning to Verify Makes Reasoning Model Efficient
This project helps make large language models (LLMs) more efficient at complex reasoning tasks, like solving math problems, by training them to verify their own steps. You provide a question, and the model generates a shorter, more accurate step-by-step solution compared to standard methods. This is ideal for researchers and practitioners working with advanced AI models who need to reduce computational costs while maintaining or improving accuracy in problem-solving.
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Use this if you need to make your large language models perform complex reasoning with fewer computational steps and higher accuracy.
Not ideal if you are looking for an off-the-shelf application for end-users, as this is a tool for developers working with LLMs.
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Python
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MIT
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Last pushed
Sep 27, 2025
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