A $500 Fine-Tune of a 9B Open Model Beat Frontier Systems on Catalog Review

An engineer reports that roughly $500 of reinforcement-learning fine-tuning on a 9-billion-parameter open model outperformed frontier systems on a narrow catalog-review task. The result is a data point in the argument that narrow tasks may not justify frontier-scale spending.