Agent Lightning

WIP nlp-llms agent-lightning agents framework llm llm-agents nlp reinforcement-learning 1 min read

Microsoft's extensible framework for RL-based training of any AI agent

Overview

Agent Lightning is a framework developed by Microsoft Research (ICLR 2026) to train AI agents using Reinforcement Learning (RL) with practically zero code changes to the original agent codebase.

The Core Concept

Traditionally, training an LLM-based agent with RL required tightly coupling the RL training logic directly into the agent’s code, or relying on messy sequence concatenation.

Agent Lightning completely decouples agent execution from RL training.

Seamless Optimization: You can take ANY existing agent framework (like LangGraph, CrewAI, or custom Python agents) and optimize them via RL data-driven techniques (fine-tuning, prompt tuning, model selection).
Use Cases: E.g., Training an AI agent to write and self-correct SQL queries through trial-and-error using Reinforcement Learning environments without rewriting the original SQL-agent script.

TODO: Add diagram showing the decoupling between the RL Engine and the Agent Executor.