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· · 来源:user百科

Summary: Can advanced language models enhance their programming capabilities using solely their initial outputs, bypassing validation mechanisms, instructor models, or reward-based training? We demonstrate positive results through straightforward self-teaching (SST): generate multiple solutions using specific sampling parameters, then refine the model using conventional supervised training on these examples. SST elevates Qwen3-30B-Instruct's performance from 42.4% to 55.3% first-attempt success on LiveCodeBench v6, with notable improvements on complex tasks, and proves effective across Qwen and Llama architectures at 4B, 8B, and 30B capacities, covering both instructional and reasoning models. Investigating this method's efficacy reveals it addresses a fundamental tension between accuracy and diversity in language model decoding, where SST dynamically modifies probability distributions—suppressing irrelevant variations in precise contexts while maintaining beneficial diversity in exploratory scenarios. Collectively, SST presents an alternative post-training approach for advancing language models' programming abilities.

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关于作者

王芳,专栏作家,多年从业经验,致力于为读者提供专业、客观的行业解读。

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