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

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The results show that even a light fine-tuning procedure improves performance from 22. 6% to 29. 7% on the IFStruct benchmark. Following the Liquid AI llama. cpp deployment docs, install llama. cpp with Homebrew and verify that llama-server is available: IFStruct Evaluation on LFM2. 5-350M (Base model) Before we begin, let's evaluate LFM2. 5-350M on the IFStruct benchmark and see whether we can reproduce the reported score of 21. 1%. IFStruct Evaluation on GRPO Tuned LFM2. 5-350M After GRPO fine-tuning, we rerun the IFStruct evaluation.

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