Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Harnessed Agentic RL: Microsoft Research Asia introduces a training paradigm in which the same agent harness used in deployment participates directly in reinforcement learning, removing the need to reimplement the agent inside the training framework. Lightweight by design: Agent Lightning v1.0 delivers a complete agent RL control plane in roughly 3,500 lines of code. Native Kubernetes support: agents run as standard Kubernetes jobs on self-managed clusters, cloud Kubernetes, or local infrastructure, with no dependency on paid commercial sandbox services. Data-efficient training recipe: an end-to-end coding agent pipeline raised Qwen3.5-9B from 41.8% to 56.4% Pass@1 on SWE-bench Verified, a 14.6 percentage point gain, using only about 6,000 training samples based on open sourced dataset. AI agents have evolved from single models to complex full-stack systems built from models, tools, and execution environments. Their capabilities increasingly depend on the agent harness that coordinates…