Improving Small Language Models for Code Generation with Reinforcement Learning from Verification Feedback
arXiv:2605.30478v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) trains language models using programmatically checkable signals such as unit-test outcomes, enabling direct optimization for functional correctness in code generation. We conduct an empirical study of RLVR for Python code generation on the MBPP benchmark using two small models (Qwen3-0.6B and Llama3.2-1B) with LoRA fine-tuning. Across multiple reward formulations such as: unit-test-only rewards, static-analysis-only shaping via the Ruff linter, and a combined reward, we compare group-based p
The rapid advancement in AI, specifically in large language models, makes iterative improvements in code generation a critical and active research area.
This research demonstrates a practical methodology to significantly improve the functional correctness of code generated by smaller language models, which is crucial for their deployment in real-world engineering tasks.
The ability to achieve higher code correctness with smaller, more efficient models means a broader range of applications and more accessible code generation AI.
- · AI developers
- · Software engineers
- · Organizations using smaller AI models
- · Open-source AI community
- · Inefficient code generation models
- · Manual code debugging
Increased adoption of AI-driven code generation tools due to improved reliability.
Reduced development costs and faster product cycles in software engineering.
Proliferation of custom, intelligent agents capable of self-correcting and generating complex software systems.
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