The core idea is to keep the LLM frozen and train everything around it (e.g. prompts, context management, tools, repair loop). The harness is one editable Python file. In each epoch, an agent proposes one diff, the diff is measured against baseline on a task panel, and a strict Pareto criterion decides whether the commit gets promoted. optimizer.step() is literally a git fast-forward or stored as git ref if the commit was not promoted.

Evaluation results show transfer learning (e.g. harness trained on SWE-Bench tasks solving Terminal Bench tasks) and general capability improvements across multiple model families using the same trained harness that even improved over the official Terminus Harness on Terminal Bench 2.0 [1].

Since this was a general problem, I took the chance to create a general PyTorch-like training framework [2]. Right now, you can train with any OpenAI-compatible API for interfacing with the task LLM and train against Terminal-Bench or SWE-Bench tasks, but you can easily extend it to support any task environments.

[1]: https://www.henrypan.com/blog/2026-07-18-harness-training/#e...

[2]: https://www.henrypan.com/blog/2026-07-18-harness-training/#g...