Post-Training Open Legal Agents With Baseten Research

This post details initial research and experiments towards open-weight legal agents, specifically post-training a 27B open-weight model using Baseten Research's pipeline. This pipeline leverages the Legal Agent Benchmark (LAB) signal and a legal-agent harness designed for long-horizon legal work. The research demonstrates that post-training can address challenges in domain expertise, cost, and governance for specialized legal agents. Experiments show that training an agent end-to-end against LAB criteria can shift its behavior towards more comprehensive document reading, similar to frontier models. A natural-language compaction strategy was implemented and benchmarked, showing significant improvements in the all-pass rate for deliverable quality. The post-training of Qwen3.5-27B using Iterative SFT (iSFT) in this compaction harness brought its performance into the closed-source frontier band on LAB, highlighting the need for co-development of post-training and harness optimization.