Simple Self-Distillation Method Improves LLM Code Generation
What Simple Self-Distillation Does
Simple self-distillation (SSD) is a post-training method where you sample solutions from a large language model with specific temperature and truncation configurations, then fine-tune the model on those samples using standard supervised fine-tuning. The key insight is that this works without needing a verifier, teacher model, or reinforcement learning.
Performance Improvements
On Qwen3-30B-Instruct, SSD improved pass@1 performance on LiveCodeBench v6 from 42.4% to 55.3%. Gains were concentrated on harder problems, and the method generalized across Qwen and Llama models at 4B, 8B, and 30B scale, including both instruct and thinking variants.
Why It Works
The researchers traced the gains to a precision-exploration conflict in LLM decoding. SSD reshapes token distributions in a context-dependent way, suppressing distractor tails where precision matters while preserving useful diversity where exploration matters. This addresses the fundamental tension between generating precise code and exploring different solution approaches.
Practical Implications
SSD offers a complementary post-training direction for improving LLM code generation that's relatively simple to implement compared to methods requiring verifiers or reinforcement learning. The approach works with existing fine-tuning infrastructure and doesn't require additional models or complex reward systems.
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