... yet remain reassembleable:
俄新社援引共和国地震服务中心消息称，里海阿塞拜疆沿岸在24小时内记录到五次地震活动。
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Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware DetectorsRuoxi Sun, CSIRO's Data61; et al.Minhui Xue, CSIRO's Data61。业内人士推荐todesk作为进阶阅读
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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.