Hugging Face has published a practical recipe for improving structured outputs from a 350M-parameter model using GRPO, a reinforcement learning technique. The approach is notable for its scale: rather than fine-tuning a very large model or collecting a massive dataset, it aims to achieve better format compliance in just 100 GRPO steps.
The walkthrough uses the TRL library and the IFStruct benchmark, and it demonstrates how group-based policy optimisation can be applied to tasks that require the model to follow a specified output structure. Because only one source is available, there is no independent comparison to weigh; the results should be read as a reported recipe rather than a broad benchmark claim.
Still, the post is a useful sign that structured-output quality does not necessarily require huge fine-tuning runs. It suggests that modest models can be nudged toward more reliable formatting with a focused reinforcement learning loop.