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README.md
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# SimpleSD-30B-instruct
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This model was produced using **Simple Self-Distillation (
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- **Self-distillation sampling:** temperature=1.6, top_p=0.8, top_k=20
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- **Evaluation sampling:** temperature=0.9, top_p=0.8, top_k=20
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## Method
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## Results
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| Model | LCBv6 pass@1 | LCBv6 pass@5 | LCBv5 pass@1 | LCBv5 pass@5 |
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| Qwen3-30B-A3B-Instruct-2507 (base) | 42.4 | 53.5 | 45.8 | 58.7 |
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## Paper
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[**Embarrassingly Simple Self-Distillation Improves Code Generation**](https://arxiv.org/abs/2604.01193)
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# SimpleSD-30B-instruct
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This model was produced using **Simple Self-Distillation (SimpleSD)**, a method that improves code generation by fine-tuning a language model on its own sampled outputs—without rewards, verifiers, teacher models, or reinforcement learning.
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- **Self-distillation sampling:** temperature=1.6, top_p=0.8, top_k=20
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- **Evaluation sampling:** temperature=0.9, top_p=0.8, top_k=20
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## Method
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SimpleSD samples solutions from the base model using non-unit temperature and top-k/top-p truncation, then fine-tunes on those samples via standard supervised learning. Despite its simplicity, SimpleSD yields large gains on competitive programming benchmarks, with improvements concentrating on harder problems. The mechanism traces to resolving a *precision–exploration conflict*: SimpleSD reshapes token distributions in a context-dependent way so that a single global decoding configuration becomes far more effective at evaluation time.
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## Results
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| Model | LCBv6 pass@1 | LCBv6 pass@5 | LCBv5 pass@1 | LCBv5 pass@5 |
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| Qwen3-30B-A3B-Instruct-2507 (base) | 42.4 | 53.5 | 45.8 | 58.7 |
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| **+ SimpleSD (this model)** | **55.3** (+12.9) | **71.6** (+18.1) | **54.3** (+8.5) | **70.7** (+12.0) |
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## Paper
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[**Embarrassingly Simple Self-Distillation Improves Code Generation**](https://arxiv.org/abs/2604.01193)
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