Simple Self-Distillation Unlocks Breakthrough Improvements in LLM Code Generation

This research demonstrates a paradigm shift: large language models can achieve substantial improvements in code generation through an elegantly simple self-distillation approach, eliminating the need for teacher models, verifiers, or reinforcement learning. The 30% performance boost across different model families suggests a universally applicable technique that practitioners can immediately implement. The theoretical insights into token distribution dynamics offer deeper understanding of LLM decoding and point toward a new post-training optimization direction.