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Microsoft's rStar2-Agent Surpasses 671B in Mathematical Reasoning: 14B Model Defeats Larger Competitor
2 week ago /
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Author：小编
Large Language Models (LLMs) now demonstrate formidable reasoning capabilities, with their prowess hinging on the innovative techniques employed during evaluation. By extending the chain of thought (CoT) methodology or augmenting the duration of thought processes, their performance can undergo a substantial enhancement. This phenomenon becomes even more pronounced when these techniques are integrated with large-scale reinforcement learning and verifiable rewards (RLVR) for optimization purposes.
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