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

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

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At a glance

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 them from outside the model. Reinforcement learning (RL) is an approach where AI systems learn through trial and error, guided by rewards and penalties for their actions. RL can make those agents better, but most agent RL systems require developers to reimplement the agent inside the training framework. That is costly, and it means the agent being trained is not quite the agent that gets deployed.

To address this, researchers at Microsoft Research Asia have introduced the Harnessed Agentic RL training paradigm and open-sourced a fully rebuilt Agent Lightning v1.0 (opens in new tab) . Compared with the original, Agent Lightning, v1.0 puts more emphasis on staying lightweight, on integrating with real harnesses, and on a complete, reproducible agent RL training pipeline.

Agent Lightning v1.0 was rebuilt around Harnessed Agentic RL, with key improvements:

Lightweight : the entire framework is about 3,500 lines of code. Agent Lightning v1.0 implements a complete Harnessed Agentic RL system in a codebase that is small and clear enough to understand, modify, and extend.

Training on a real agent harness : agents reach the model through the large language model (LLM) proxy in Agent Lightning v1.0, leaving existing harness code unchanged.

Native Kubernetes support : agents run directly as Kubernetes jobs, without external commercial sandbox services. Self-managed clusters and local infrastructure alike can support rollouts at scale.

A complete coding agent training example : an end-to-end pipeline built on Qwen3.5-9B raised Pass@1 on SWE-bench Verified from 41.8% to 56.4%, an absolute gain of 14.6 percentage points, using only about 6,000 training samples.

The limits of traditional agentic RL

Traditional agentic RL assumes the training framework owns the interaction loop with the environment. In a ReAct-style loop, the model generates an action, the environment returns an observation, the observation is appended to the context, and the model generates the next action, so the whole rollout maps onto one continuous token trajectory. Early RL systems such as verl, AReaL, and slime were built this way, which meant training an agent required rebuilding its loop inside the RL framework.

Real harnesses have outgrown that assumption. Coding agents such as mini-SWE-agent, OpenHands, OpenCode, Claude Code, and Codex each bring their own context management, tool protocols, execution logic, and dependencies, as do general-purpose agent systems. Rebuilding one for training is expensive, and the rebuilt agent may no longer behave in the same way as the deployed agent.

Agent Lightning takes a different route. It places an LLM proxy between the agent and the model. The agent continues to run as before: simply point the endpoint that previously called the model API at Agent Lightning, and the training framework can observe and record its model calls. In v1.0, the researchers go further and formally define this paradigm as Harnessed Agentic RL: whichever agent harness is used in deployment is the harness that takes part directly in reinforcement learning during training (Figure 1).

Figure 1. Traditional agentic RL compared with Harnessed Agentic RL. In traditional agentic RL, the training framework manages the environment and the agent loop. In Harnessed Agentic RL, the harness manages both.

Four challenges in training with real harnesses

A core difference between Harnessed Agentic RL and traditional agentic RL is that the environment interaction loop is handled by the agent harness rather than the training framework. The training system can only observe a series of LLM request and response pairs, so a single rollout may be split into a variable number of training samples. This brings four key challenges:

Retokenization and sample merging : Harnesses keep context as text, but RL training needs the token IDs sampled during the rollout. Passing text through the chat...