Harness Scaling trains agentic RL across diverse harnesses
Original title: Harness Scaling for Generalizable Agentic Reinforcement Learning
A factorized training subset improved transfer to unseen white-box setups and external black-box harnesses.
The paper introduces Harness Scaling, an agentic reinforcement-learning approach that trains models across diverse execution harnesses instead of one fixed configuration. Authors build a factorized harness space, pick a compact subset with balanced factorial coverage, and mix global coverage with controlled variation on the same task. They report better generalization to unseen white-box configurations across benchmarks, model scales, and RL algorithms, and stronger transfer to unseen black-box harnesses than single-harness training.
Unconfirmed: Generalization gains are the authors' reported results; independent replication is not stated.
- Training uses a compact subset of a factorized harness space.
- Cross-harness training pairs global coverage with controlled task variation.
- White-box variation transferred to unseen external black-box harnesses.
- Gains are reported across benchmarks, model scales, and RL algorithms.
From the original article
1 min read at huggingface.co
Agent harnesses have become a central component of modern agentic systems, shaping how models use tools, manage context, and coordinate long-horizon interaction. Realizing the benefits of these harnesses depends on how effectively the underlying model can make use of them, motivating reinforcement learning directly through the harness.
By Fei Bai, Huatong Song, Chuan Hao · huggingface.co
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