Embarrassingly Simple Self-Distillation Improves Code Generation

Researchers at **Apple Machine Learning Research** introduced **simple self-distillation** (**SSD**), a method enabling a large language model to improve its code generation performance using only its own raw outputs. When evaluated on **Qwen3-30B-Instruct**, **SSD** raised the model's **pass@1** score on **LiveCodeBench v6** from 42.4% to 55.3%.

The mechanics of **SSD** involve sampling solution candidates directly from the model using specified **temperature** and **truncation** configurations. The model is then fine-tuned on those sampled outputs using standard **supervised fine-tuning**. The procedure operates without an external **verifier**, a separate **teacher model**, or **reinforcement learning**.

For engineers and builders, **SSD** demonstrates that post-training improvements do not require integrating external feedback tools or larger auxiliary models. Evaluation data indicates that these self-distillation improvements are concentrated, with **gains concentrating on harder problems** within the benchmark set.

In terms of methodology context, **simple self-distillation** provides a streamlined alternative to post-training workflows that depend on **teacher models**, code **verifiers**, or **reinforcement learning**. Elevating **Qwen3-30B-Instruct** from 42.4% to 55.3% **pass@1** proves that raw model sampling paired with standard fine-tuning can drive significant accuracy increases.

The practical takeaway is that practitioners can implement **SSD** by combining targeted **temperature** and **truncation** sampling with **supervised fine-tuning**. Key parameters to monitor next include whether **SSD** achieves similar gains across other model families beyond **Qwen3-30B-Instruct** and across future evaluations on **LiveCodeBench v6**.