Kyle Corbitt

This talk details the process of building a reliable AI agent using reinforcement learning (RL), focusing on practical lessons learned from the ART E project, an email assistant. The core thesis is that while RL can significantly improve agent performance beyond prompted models, it's crucial to start with a strong prompted baseline, carefully design the training environment and reward functions, and be vigilant against reward hacking. The project demonstrates how RL can lead to substantial gains in accuracy, cost reduction, and latency improvements for specialized tasks.

This talk details how Method scaled its AI agent operations to over 500 million agents with a small engineering team. Initially, they faced challenges with manual data aggregation processes and later with the high costs and limitations of using large language models like GPT-4 for parsing unstructured financial data. The solution involved fine-tuning smaller, more efficient models to meet specific production requirements for accuracy, latency, and cost.