Harvey post-trains RLM agents for M&A diligence, lifting benchmark pass rate from 23% to 62%
lateinteraction · x · 2026-09-09
Legal AI company Harvey, partnering with Baseten, published research on post-training recursive language model (RLM) agents to run end-to-end M&A diligence.
Key points:
- They built an RLM harness where a root agent searches the data room, delegates document review to sub-agents, and orchestrates a final diligence memo
- Distributed review lets it work through data rooms of up to 80M tokens
- On their synthetic LAB Diligence benchmark, the harness raised average rubric pass rates from 23% to 62%
- A key finding: model-harness co-optimization matters — the root agent processes only 1-4% of tokens (sub-agents handle 96-99%) yet its choice still materially moves performance
Strong evidence that co-optimizing model and harness pays off in long-horizon real-world tasks.
Related event: Harvey's post-trained RLM agent boosts M&A due diligence pass rate(3 posts)→
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