Supervised learning
Hugging Face shares GRPO fine-tuning recipe for structured outputs in 100 steps
The tutorial demonstrates how developers can use Group Relative Policy Optimization (GRPO) to fine-tune a small 350M model to reliably generate formatted JSON in just 100 training steps.
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The short version
- Hugging Face published a tutorial detailing how to use Group Relative Policy Optimization (GRPO) via the TRL library to fine-tune a small 350-million parameter model.
- In just 100 steps using about 500 samples, the method increased the JSON compliance and format pass rate of the Liquid LFM-350M model from 18.0% to 31.9% on the IFStruct benchmark.
The tutorial demonstrates how developers can use Group Relative Policy Optimization (GRPO) to fine-tune a small 350M model to reliably generate formatted JSON in just 100 training steps.