Agent Lightning v1.0: Towards Harnessed Agentic RL

TL;DR - Agent Lightning v1.0 is a lightweight framework for training agents with reinforcement learning while their deployment harness retains control of tools, context, and environment interaction. It provides a reproducible coding-agent pipeline that raises Qwen3.5-9B’s SWE-bench Verified score from 41.8% to 56.4%.

• Defines “harnessed agentic RL,” where trainers observe LLM request-response sequences rather than owning the environment loop.
• Supports arbitrary agent harnesses through a disaggregated architecture and LLM endpoint proxy in roughly 3,500 lines of code.
• Addresses training challenges including retokenization, sample merging, advantage calculation, loss normalization, and backend scheduling.
• Achieves a 14.6-point absolute SWE-bench Verified gain using 6K training examples and modest compute.