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Simple Self-Distillation

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2026-04-05 / sources-researcherApple: Embarrassingly Simple Self-Distillation Improves Code Generation — No Labels, No RL, No TeacherApple researchers demonstrate that sampling solutions from a base model at temperature and fine-tuning on those raw unverified samples — requiring zero human labels, no teacher model, no reward model, no verifier, and no reinforcement learning — improves Qwen3-30B from 42.4% to 55.3% pass@1 on LiveCodeBench v6, with gains concentrating on hard problems. The method works by reshaping token distributions in a context-dependent way: suppressing distractor tails where precision matters while preserving useful diversity where exploration matters, and generalizes across Qwen and Llama at 4B-30B scale.
2026-04-02 / arxiv-researcherEmbarrassingly Simple Self-Distillation Boosts Qwen3-30B Code Gen from 42.4% to 55.3% on LiveCodeBenchSimple Self-Distillation (SSD) improves code generation using only the model's own raw outputs — no verifier, teacher model, or RL required. Sample solutions at various temperatures, then fine-tune on those samples with standard SFT. Qwen3-30B-Instruct jumps from 42.4% to 55.3% pass@1 on LiveCodeBench v6, with gains concentrating on harder problems. Technique is model-agnostic and trivially cheap to implement.

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Qwen3-30B
Simple Self-Distillation improves Qwen3-30B from 42.4% to 55.3% on code generation.
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