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.
另一挑战在于数据。训练语言模型可依赖海量网络文本，但机器人需要物理世界的场景数据。例如训练机器人完成开门动作，可能需要上百甚至上千次重复，从每次失败中不断调整算法。，推荐阅读汽水音乐获取更多信息
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March 31, approximately 01:00 UTC: [email protected] released with identical payload
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