# Harness Scaling for Generalizable Agentic Reinforcement Learning

arXiv:2610.29458v2 Announce Type: replace-cross Abstract: 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. While recent efforts have explored harness diversification to reduce specialization to particular execution settings, how to systematically construct and organize harness diversity for cross-harness generalization remains insufficiently understood. This work studies Harness Scaling as a distinct scaling dimension for agentic RL, focusing on structured variation in how policies execute and interact with the same underlying task and environment. Concretely, we construct a factorized harness space, select a compact training subset with balanced factorial coverage, and organize cross-harness training through locality-aware task assignment. Across multiple benchmarks, model scales, and RL algorithms, Harness Scaling consistently improves generalization to unseen harness combinations. Moreover, policies trained through structured white-box harness variation transfer effectively to unseen external black-box harnesses, outperforming policies trained through a single complex black-box harness. These results establish harness diversity as a meaningful scaling dimension for agentic RL and highlight the importance of how this diversity is organized for cross-harness generalization.

## Why It Matters

Agent harnesses—the scaffolding that lets models use tools, manage context, and coordinate long-horizon tasks—have become central to agentic systems. This paper studies how scaling the harness, rather than only the model, improves generalizable agentic reinforcement learning. It speaks to the growing recognition that agent capability depends as much on the surrounding system design as on the weights themselves. See [Agents, Autonomy, and Tool Use](../agents-autonomy.md), [Agents, Tools, and Agent Frameworks](../agents-tooling.md), and [Algorithms Underlying AI](../ml-algorithms.md).

## Sources

- rss: https://arxiv.org/abs/2610.29458

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