Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps
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Hugging Face demonstrates how to train a small AI model (350 million parameters) to produce better structured outputs, such as valid JSON.
Using a training method called GRPO with only around 500 training examples and 100 steps, the model improved from 22.6 percent to 29.7 percent on the IFStruct test. The key achievement was getting the model to produce valid JSON code — the percentage of correct JSON responses increased from 18 percent to nearly 32 percent. The method is inexpensive and works on free GPU resources, showing that even small models can become sufficiently reliable for real-world use if trained correctly.
A short GRPO run with about 500 samples and 100 steps can lift a small 350M parameter model from 22.6% to 29.7% on IFStruct.
Vibekollen prepared this summary with AI from the original publication. The content belongs to Hugging Face.
