Infini-AI-Lab Open-Sources Hermit for Model-Agnostic Agent Learning
Infini-AI-Lab released Hermit, an Apache-2.0 framework enabling AI agents to learn continuously via a trainable 'Harness Adapter' without altering model weights. Using an RL loop and Git-based versioning for instructions and skills, it improved accuracy on CL-Bench and OfficeQA, though results rely on single runs and incur higher latency costs.
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▲ Marktechpost AI · 2026-10-29 08
▲ Infini-AI-Lab just open-sourced Hermit, a framework that lets AI agents keep learning without touching the model or the harness.
The key idea is a "harness adapter": a trainable external layer of instructions, skills, memory, tools and code. An RL-style loop runs tasks, a judge scores them, and an Evolver proposes a new adapter version. Each version is checked and committed to git. The model's weights never change.
With GLM 5.3 Flash, the MWM adapter reaches 0.795 accuracy on CL-Bench database exploration, vs 0.580 for the in-context baseline. On OfficeQA, Harness² lifts accuracy from 0.541 with no adapter to 0.651. It is a trade-off though: most results are single runs, and Harness² costs about $0.16 and 7 minutes per question.
It ships 5 methods (GEPA, SkillOpt, MWM, Harness², Continual Harness), deploys to Codex, Claude Code and Pi, and is Apache-2.0 via pip install hermit-harness......
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