Kobie Crawford

This talk challenges the assumption that larger AI models are always superior, particularly for enterprise use cases requiring reliability and security. It presents research demonstrating that a smaller, 4-billion parameter model, when fine-tuned with Reinforcement Learning (RL) and high-quality data, can outperform a significantly larger 235-billion parameter model on a tool-use task for financial analysis. The core thesis is that focusing on improving model behavior and tool discipline through targeted data and training can yield substantial performance gains, often more effectively and efficiently than simply increasing model size.