arXiv:2604.01193v2 Announce Type: replace Abstract: Can a large language model (LLM) improve at code generation using only its own raw outputs, without a verifier, a teacher model, or reinforcement learning? We answer in the affirmative with simple self-distillation (SSD): sample solutions from the model with certain temperature and truncation configurations, then fine-tune on those samples with standard supervised fine-tuning. SSD improves Qwen3-30B-Instruct from 42.4% to 55.3% pass@1 on LiveCodeBench v6, with gains concentrating on harder problems, and it generalizes across Qwen and Llama mo
The continuous drive to improve LLM performance for specialized tasks like code generation, combined with increasing computational constraints, motivates exploration of more efficient self-improvement methods.
This development allows LLMs to significantly improve their code generation capabilities without external data, teachers, or complex reinforcement learning, offering a more scalable and autonomous path to model refinement.
LLMs can now leverage their own outputs for substantial performance gains in coding, reducing the reliance on costly human annotation or sophisticated external feedback loops for specialized tasks.
- · AI developers
- · Companies using LLMs for code generation
- · Open-source LLM communities
- · Software developers
- · Providers of code-specific fine-tuning datasets
- · Companies relying solely on external verifiers for code LLM improvement
Increased performance and efficiency of code-generating LLMs across various platforms and applications.
Accelerated development of AI-powered coding assistants and autonomous code generation tools, potentially lowering the barrier to entry for software creation.
Enhanced AI 'self-improvement' capabilities could lead to more rapid and less human-dependent evolution of specialized AI agents, expanding the scope of autonomous AI applications.
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