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

What changed

Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps Structured output is one of the most common real-world tasks for LLMs, yet most benchmarks fold it into broader reasoning or extraction scores rather than measuring it on its own. Whether a model reliably returns valid, parseable output in the requested format and shape — schema compliance — is often what decides whether it can be wired into a downstream system at all. Note that the training pipeline described here is not the one used to train the RL model described in the IFStruct blog.

Why it matters

A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.

How it compares

Related prior coverage to compare against:

• Introducing Structured Outputs in the API
• Model Distillation in the API
• Fine-tuning now available for GPT-4o

• Introducing Structured Outputs in the API similarity 0.36
• Model Distillation in the API similarity 0.22
• Fine-tuning now available for GPT-4o similarity 0.20