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

Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps Published September 3, 2026 Update on GitHub Upvote 134 Leonie Monigatti iamleonie LiquidAI ben burtenshaw burtenshaw Sergio Paniego sergiopaniego This guide is a fully public, inexpensive recipe for making a small model substantially better at structured-output compliance. We fine-tune LFM2.5-350M with Group Relative Policy Optimization (GRPO) using the TRL library and evaluate it on the IFStruct benchmark .

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• Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps Published September 3, 2026 Update on GitHub Upvote 134 Leonie Monigatti iamleonie LiquidAI ben burtenshaw burtenshaw Sergio Paniego sergiopaniego This guide is a fully public, inexpensive recipe for making a small model substantially better at structured-output compliance.
• We fine-tune LFM2.5-350M with Group Relative Policy Optimization (GRPO) using the TRL library and evaluate it on the IFStruct benchmark .
• The full run takes around 500 samples and 100 training steps, small enough for a free-tier Colab or Kaggle GPU, and is available on GitHub .
• The results show that even a light fine-tuning procedure improves performance from 22.6% to 29.7% on the IFStruct benchmark.
• 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.