3月6日，俄罗斯外交部发言人玛丽亚·扎哈罗娃表示，莫斯科已就乌克兰武装部队无人机问题向波罗的海国家发出警告。“如果这些国家的政权尚有理智，就应当听取警告。否则他们将承担相应后果。”这位外交官表示。
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Summary: Can large language models (LLMs) enhance their code synthesis capabilities solely through their own generated outputs, bypassing the need for verification systems, instructor models, or reinforcement algorithms? We demonstrate this is achievable through elementary self-distillation (ESD): generating solution samples using specific temperature and truncation parameters, followed by conventional supervised training on these samples. ESD elevates Qwen3-30B-Instruct from 42.4% to 55.3% pass@1 on LiveCodeBench v6, with notable improvements on complex challenges, and proves effective across Qwen and Llama architectures at 4B, 8B, and 30B capacities, covering both instructional and reasoning models. To decipher the mechanism behind this elementary approach's effectiveness, we attribute the enhancements to a precision-exploration dilemma in LLM decoding and illustrate how ESD dynamically restructures token distributions—suppressing distracting outliers where accuracy is crucial while maintaining beneficial variation where exploration is valuable. Collectively, ESD presents an alternative post-training pathway for advancing LLM code synthesis.
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