CODEBLOCK: Learning to Supervise Code at the Right Granularity

In three linesCodeBlock is a structure-aware sparse supervision framework for code LLM fine-tuning. It selects syntactically coherent code blocks rather than isolated tokens, estimating utility via generalized cross-entropy and data-flow signals. On 6 code-generation benchmarks, CodeBlock outperforms full-token SFT while using only 1.9% of supervised response tokens.

Summary generated by Claude — human-verified