Harness Scaling for Generalizable Agentic Reinforcement Learning

Key points

• Agent harnesses have become a central component of modern agentic systems, shaping how models use tools, manage context, and coordinate long-horizon interaction.1
• 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.1
• Concretely, we construct a factorized harness space, select a compact training subset with balanced factorial coverage, and organize cross-harness training to combine global coverage with controlled variation for the same task.1
• Across multiple benchmarks, model scales, and RL algorithms, Harness Scaling consistently improves generalization to unseen white-box configurations.1

Sources (2)

• [1]Harness Scaling for Generalizable Agentic Reinforcement Learning
  Hugging Face Daily Papers · Oct 21, 12:00 AM

  To address this, we introduce Harness Scaling, a new scaling dimension for agentic RL that extends training from a single harness to diverse execution configurations.

  Agent harnesses have become a central component of modern agentic systems, shaping how models use tools, manage context, and coordinate long-horizon interaction.

• [2]Harness Scaling for Generalizable Agentic Reinforcement Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 09:15 AM · same content

Extractive summary: sentences quoted from the sources.