Polar: Agentic RL on Any Harness at Scale
The article discusses a new framework called Polar designed for scalable asynchronous reinforcement learning (RL) across various agent harnesses. Polar simplifies the integration of custom harnesses into RL environments while maintaining crucial training signals. It has been validated through improvements in performance on software-engineering tasks using popular coding harnesses.
- ▪Polar is a rollout framework that treats agent harnesses as black boxes to enhance RL training.
- ▪The framework improves compute utilization for long-running agent workloads and is agnostic to specific harnesses and algorithms.
- ▪Validation of Polar showed significant performance improvements on software-engineering tasks with various coding harnesses.
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Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2605.24220 (cs) [Submitted on 22 May 2026] Title:Polar: Agentic RL on Any Harness at Scale Authors:Binfeng Xu, Hao Zhang, Shaokun Zhang, Songyang Han, Mingjie Liu, Jian Hu, Shizhe Diao, Zhenghui Jin, Yunheng Zou, Michael Demoret, Jan Kautz, Yi Dong View a PDF of the paper titled Polar: Agentic RL on Any Harness at Scale, by Binfeng Xu and 11 other authors View PDF HTML (experimental) Abstract:Reinforcement learning for language agents increasingly depends on custom harnesses that manage long-running context, multi-turn tool use and multi-agent orchestration. However, porting these harnesses into RL environment interfaces remains difficult and often loses important training signals.
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