<ul><li>OpenPipe introduces ART·E, an open-source research agent for email that outperforms o3 in accuracy, latency, and cost.</li><li>ART·E focuses on accuracy, responsiveness, and computational efficiency using reinforcement learning (RL) to fine-tune large language model (LLM) agents for email-related tasks.</li><li>The architecture of ART·E includes retriever module, LLM policy head, and evaluation pipeline trained using Proximal Policy Optimization (PPO) regime for improved performance.</li><li>Benchmarking against o3 agent, ART·E shows +12.4% response accuracy, 5× faster average latency, and 64× cheaper inference cost, providing a favorable cost-performance tradeoff.</li></ul>

Reinforcement Learning for Email Agents: OpenPipe’s ART·E Outperforms o3 in Accuracy, Latency, and Cost

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