Hugging Face has released a detailed guide on fine-tuning a 350M parameter model using Group Relative Policy Optimization (GRPO) and the TRL library to enhance structured output compliance. The guide, published on September 3, 2026, provides a fully public and inexpensive recipe for improving the performance of small models in structured output tasks. The fine-tuning process involves using a LoRA adapter and training the model for 100 steps with 8 generations per prompt group, suitable for a free-tier 16 GB GPU. The training procedure improves the model's performance on the IFStruct benchmark from 22.6% to 29.7%. The guide also includes the implementation details of reward functions and training configurations, making it accessible for researchers and developers to replicate the results.
Source: Hugging Face
