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

A small 350-million parameter language model can be significantly improved at generating reliable, structured outputs like JSON through a targeted fine-tuning process. Using a technique called Group Relative Policy Optimization (GRPO), the model was trained in just 100 steps on an inexpensive, free-tier GPU. This brief training boosted the model's performance on the IFStruct schema compliance benchmark from an initial score of 22.6% to an impressive 29.7%. The entire process required only 500 training samples, demonstrating how a lightweight approach can yield substantial gains in output reliability for real-world applications.