New Microsoft paper says agent training should happen inside the agent's normal operating setup. Otherwise, you may be training something different from what you actually deploy.

"In traditional agentic RL, the training engine owns the environment interaction loop. In harnessed agentic RL, the harness owns this loop, while the training engine observes only a sequence of LLM request-response pairs."

Microsoft’s solution is Agent Lightning v1.0, a lightweight framework for harnessed agentic RL that trains an existing agent through its real deployment harness, instead of rebuilding the agent loop inside the RL trainer.

Agent Lightning v1.0 sits between the agent harness and the model, recording the harness’s LLM calls and feeding them into the RL trainer without taking over the harness itself.

So it makes RL work correctly with arbitrary deployment-time harnesses, including the messy cases where 1 rollout becomes multiple training samples.

– arxiv. org/abs/2608.17528

Title: "Agent Lightning v1.0: Towards Harnessed Agentic RL"