Tiny $500 RL fine‑tune lets a 9B open model beat frontier LLMs on catalog review
A team spent $500 to reinforcement‑learn a 9‑billion‑parameter open‑source model, and it outperformed leading proprietary models on a catalog‑review benchmark. The result shows tiny teams can match state‑of‑the‑art performance without massive compute budgets, dramatically lowering the entry barrier for AI‑driven startups.