Microsoft Research open-sources Agent Lightning v1.0, a 3,500-line agentic RL framework

Microsoft Research Asia released Agent Lightning v1.0, a lightweight framework that implements a complete agentic reinforcement learning control plane in roughly 3,500 lines of code. It introduces Harnessed Agentic RL, where the same agent harness used in deployment participates directly in training via an OpenAI-compatible LLM proxy, avoiding the need to reimplement the agent inside the training framework. The framework runs agents as standard Kubernetes jobs instead of paid commercial sandbox

Why it matters: This gives AI engineers an open, reproducible way to fine-tune real deployment harnesses with RL, backed by a concrete SWE-bench gain and a Kubernetes-native design that avoids sandbox costs.