Improving Small Language Models for Code Generation with Reinforcement Learning from Verification Feedback
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 policy optimization variants (GRPO and GSPO) and evaluate both functional correctness and behavioral diagnostics. In our experimental setting, RLVR ...
| Authors | Egor Skopin, Evgeny Kotelnikov |
|---|---|
| Primary category | cs.SE |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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