Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps

Fine-tuning is a technique to improve an AI model's performance on specific tasks. This guide demonstrates how to fine-tune a small 350-million-parameter model to better produce structured outputs like JSON and YAML that follow required formats and schemas, which is important because real-world systems often need AI models to return data in exact, parseable formats. The authors used a training method called Group Relative Policy Optimization with about 500 training samples and 100 steps, which improved the model's structured output compliance from 22.6% to 29.7% on a benchmark test, and this process is small enough to run on free cloud computing resources.