Harvey Tenet Research Preview

This post introduces Harvey Tenet, a new post-trained open-weight model based on Kimi K3, developed in collaboration with Fireworks. It details the model's training methodology using asynchronous reinforcement learning (RL) and group-sequence policy optimization (GSPO), focusing on improving performance and cost-efficiency for long-horizon, agentic legal tasks. The post highlights initial results on legal benchmarks (LAB, APEX Agents, Redline Bench) and explores specialized capabilities in M&A Diligence (using Recursive Language Models - RLM), Review Tables (structured data extraction), and Firm Knowledge.