Baseten and Harvey boost M&A diligence agent pass rate from 29% to 63% with RL on RLM harness
baseten · x · 2026-09-09
Baseten partnered with legal AI company Harvey on model-harness co-optimization for end-to-end M&A diligence: applying RL post-training to a Qwen 122B-A10B root agent inside an RLM harness lifted the pass rate from 29% to 63%.
- The RLM (recursive language model) harness lets the agent decompose long-horizon legal workflows into tool calls and subtasks
- Key takeaway: swapping models alone isn't enough—co-optimizing model and harness for the task drives large end-to-end gains
- The team is now scaling RL training with GLM-5.3, a frontier open-weight model, and observing similar uplift
- This extends Harvey's Tenet research preview toward its legal agent products
Related event: Harvey's post-trained RLM agent boosts M&A due diligence pass rate(3 posts)→
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