arXiv:2606.18286v1 Announce Type: new Abstract: Supervised fine-tuning of code LLMs typically applies uniform cross-entropy loss to all response tokens, implicitly assuming that every token provides equally useful learning signal. Recent token-level selection methods challenge this assumption in natural-language SFT by supervising only high-value tokens. However, directly transferring token-level masking to code can break syntactically and semantically coherent program units, because code depends on structural completeness and definition-use relations. We therefore propose CodeBlock, a structu
The continuous improvement and fine-tuning of large language models for code generation demand more efficient and targeted training methods to overcome the limitations of uniform supervision.
Improving the efficiency and quality of code LLMs through granular supervision directly impacts the productivity of software development, potentially accelerating AI agent development and reducing computational costs.
The methodology for training code-specific large language models shifts towards more nuanced, structure-aware supervision, moving beyond simple token-level cross-entropy loss.
- · AI model developers
- · Software development companies
- · AI agent developers
- · Cloud computing providers
- · Companies with inefficient code generation workflows
Code LLMs become more efficient and produce higher quality, more robust code with less training data.
Accelerated development of AI-driven software, reducing time-to-market for new applications and enhancing developer productivity.
Enhanced AI agents capable of autonomous software development and maintenance, leading to significant shifts in the software engineering labor market.
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Read at arXiv cs.LG