@misc{he2026agent,
author = {He, Zhiyuan and Zhang, Siwei and Zhou, Zhiwen and Yang, Yuqing and Kang, Yu and Zhang, Yuge and Qiu, Luna K. and Tsui, Tin Yan and Xu, Jiahang and Luo, Chong},
title = {Agent Lightning v1.0: Towards Harnessed Agentic RL},
howpublished = {arXiv},
year = {2026},
month = {August},
abstract = {Modern agents operate inside agent harnesses that manage tools, context, and control flow, making the harness a critical part of the agent system. Our original Agent Lightning introduced a disaggregated architecture that connects arbitrary agents to RL training through an LLM endpoint proxy, an approach later adopted by frameworks such as verl Uni-Agent, AReaL 2.0, slime, and Polar. We refer to this paradigm as harnessed agentic RL, where the deploy-time harness directly participates in model post-training. Harnessed agentic RL differs fundamentally from traditional agentic RL: the harness, rather than the training engine, owns the environment interaction loop, while the trainer observes only sequences of LLM request-response pairs. This introduces challenges in retokenization, sample merging, advantage calculation, loss normalization, and backend scheduling, which can substantially affect training stability and effectiveness. We present Agent Lightning v1.0, a lightweight framework for harnessed agentic RL implemented in approximately 3,500 lines of code. It supports arbitrary agent harnesses and serves as a practical testbed for studying these challenges. We evaluate it on instruction-following, search, and coding agents, and provide a complete reproducible pipeline for coding-agent RL. Using only 6K training examples and modest compute, RL improves Qwen3.5-9B on SWE-bench Verified from 41.8% to 56.4%, a 14.6-point absolute gain. We release the complete workflow and training scripts to facilitate reproducible research on harnessed agentic RL.},
url = {http://approjects.co.za/?big=en-us/research/publication/agent-lightning-v1-0-towards-harnessed-agentic-rl/},
}