$500 reinforcement‑learning fine‑tune of 9B open model outperforms frontier systems on catalog review

Researchers applied a $500 reinforcement‑learning fine‑tune to a 9‑billion‑parameter open model. The fine‑tuned model was evaluated on catalog review benchmarks. It achieved higher accuracy than several frontier proprietary models. The cost efficiency highlights the potential of inexpensive tuning methods. The study demonstrates competitive performance without large budgets. Results suggest open models can rival commercial alternatives. The work was documented on the Fermisense blog. Future work may explore scaling the approach to other domains.