Gabe Pereyra(@gabepereyra):Awesome work by @nikogrupen @ItsJulioPereyra and team with @baseten. Together with @baselabs, we built a recursive language model (RLM) harness that lets a root agent delegate document review to sub-agents and combine their findings into a diligence memo. Post-training Qwen 3.5 in this RLM harness more than doubled rubric pass rate on LAB Diligence tasks, from 29.9% to 63.0%, and increased review coverage from 62% to 96% of all documents in a dataroom. We’re also doing an RL scale up run with GLM-5.3 and will share the results soon and believe model-harness co-optimization is a viable path to automating end-to-end legal tasks like M&A diligence.

2026.09.08 17:04

Awesome work by @nikogrupen @ItsJulioPereyra and team with @baseten. Together with @baselabs, we built a recursive language model (RLM) harness that lets a root agent delegate document review to sub-agents and combine their findings into a diligence memo. Post-training Qwen 3.5 in this RLM harness more than doubled rubric pass rate on LAB Diligence tasks, from 29.9% to 63.0%, and increased review coverage from 62% to 96% of all documents in a dataroom. We’re also doing an RL scale up run with GLM-5.3 and will share the results soon and believe model-harness co-optimization is a viable path to automating end-to-end legal tasks like M&A diligence.