Harness Training shifts agent improvement from the model to the workbench around it

A new r/MachineLearning project frames agent improvement as a harness problem rather than a model problem. The author says the work evolved from “Agent-driven Self-improving Harness” into “Harness Training”: keep the task LLM frozen, train the surrounding harness once against a task environment, and then use that harness to improve capability across models and environments. The proposal is early, but it lands on a real pressure point in agent systems.

The PyTorch-like framing is useful because it treats helper functions, state handling, retries, and action selection as things that can be optimized rather than hand-written forever. In many agent workflows, the model is only one part of the result. The surrounding software decides which tools are visible, how observations are compressed, how failures are recovered, and when a partial result is good enough to continue. Those choices often determine whether a strong model behaves like a reliable worker or an expensive autocomplete loop.

The cost argument is the main reason this is worth watching. Fine-tuning a large model for every task is expensive, and prompt-by-prompt tuning does not scale well across teams. A reusable harness could carry operational knowledge between models and related environments. That does not make generalization automatic. The important tests will be whether a trained harness avoids overfitting to one benchmark, remains inspectable, and transfers to tasks where the observation and action spaces are only partially similar.

The community interest reflects a broader shift in agent research. The next gains may come less from asking for a better model and more from learning the execution layer around the model. Harness Training is still a project post, not a settled architecture. Even so, it points at a practical research direction: treat agent reliability as software infrastructure that can be trained, measured, and reused.

Project repository