I did something similar. It started off as a "self-improving agent" project, inspired by autoresearch, then later on I reframed it as "harness training" (discre

I did something similar. It started off as a "self-improving agent" project, inspired by autoresearch, then later on I reframed it as "harness training" (discrete program search) borrowing the mental model from ML training. I "trained" the harness on a subset of Terminal-Bench 2.0 tasks while keeping the LLM (local Qwen3.6-35B A3B) frozen. Making LLM inference and the task environments fully deterministic was necessary for clean credit assignment. I learned this the hard way after spending the initial 1 month on experiment noise. My final results showed that on the full 89-task Terminal-Bench 2.0 suite, the trained harness matched or beat the official Terminus 2 harness for four LLMs that it never collaborated with during training (e.g. GPT-OSS-120B score increased from 18.7% to 36%, while using 55% fewer input tokens per solve). A harness trained only on SWE-bench improved Terminal-Bench scores too. Here's the write-up: https://www.henrypan.com/blog/2026-07-18-harness-training/ https://www.henrypan.com/blog/2026-07-18-harness-training/ I packaged the training loop as a PyTorch-style framework. https://github.com/workofart/harness-training https://github.com/workofart/harness-training