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SLMFix: Leveraging Small Language Models for error fixing with Reinforcement Learning
ref: https://arxiv.org/pdf/2511.19422 Summary The paper suggest to train small language model (SLM) repair code for least known programming languages. The report over 95% static-validation pass rate and improvements over direct LLM fine-tuning and self-correction prompting (agentic frameworks). The paper include building training pairs from LLM-generated programs, applies LoRA for initialization, then PPO reinforcement learning with…
Kim 2 ML 