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

This post details the process of fine-tuning the LFM2.5-350M model using Group Relative Policy Optimization (GRPO) with the TRL library to improve its structured output compliance. It outlines data augmentation strategies to bridge gaps between training and evaluation datasets, the configuration of LoRA adapters targeting specific model modules, and the definition of three reward functions (JSON format, field count, schema validation) to guide the fine-tuning process. The post provides concrete training arguments and demonstrates the evaluation of the fine-tuned model against the IFStruct benchmark, showing a performance improvement from 22.6% to 29.7%. It also details the conversion of the merged model to GGUF format for local serving with llama.cpp.