We partnered with @baseten to post-train recursive language model (RLM) agents for M&A diligence. We found that model-harness co-optimization meaningfully improves agent performance in long-horizon environments, bringing us closer to agents that can complete M&A diligence end-to-end. We started by building an RLM harness that lets a root agent search a data room, delegate document review to sub-agents, and orchestrate the sub-agents' work to produce a final diligence memo. By distributing document review across sub-agents, the RLM harness can work through thousands of documents in data rooms containing up to 80M tokens. On LAB Diligence, our synthetic M&A diligence benchmark, the RLM harness raised average rubric criteria pass rate across models from 23% to 62%. Despite the root agent processing just 1-4% of the tokens in diligence (with sub-agents processing 96-99%), the choice of root agent had a much larger effect on performance than choice of sub-agents. This led us to focus on the root agent, training two models separately to improve diligence performance. First, we trained a GLM-5.2 root model in the RLM harness using self-distillation SFT. We selected GLM-5.2 diligence runs with high data room coverage and fine-tuned the model on these trajectories. This increased benchmark performance from 46% to 60%. Second, we trained a base Qwen3.5-122B-A10B using GRPO within the RLM harness, keeping the sub-agent models fixed. This raised rubric criteria pass rate from 30% to 63% on 50 held-out data rooms. In both cases we saw major qualitative shifts in agent behavior. The trained root agent learned to delegate more thoroughly and write its memo incrementally as sub-agent findings came back. Data room coverage rose from 62% to 96%. And the root agent learned to scale the size of its agent team proportionally to the size of the data room. We are now scaling RL training with GLM-5.3, a frontier open-weight model, as the root agent. A small amount of training gives an increase in pass rate from 51% to 59%. @nikogrupen walks through these experiments with more on harness design, training setup and results, and what we're exploring next: