$500 RL fine-tune of 9B model beats frontier models on catalog review

A $500 reinforcement learning fine-tune of a 9-billion-parameter open model outperformed frontier models like GPT-4 on a catalog review task. The approach used a small curated dataset and achieved higher accuracy with lower cost. This demonstrates that targeted fine-tuning can rival massive proprietary models for specific enterprise applications.