New research optimizes data scaling for agentic reinforcement learning

A new research paper explores how to optimize data scaling in agentic reinforcement learning (RL) by treating data as a structured design space rather than a single token count. The study identifies three key cardinality axes for agentic data: task, environment, and trajectory, along with a reward-density control variable. Across various model sizes, families, domains, and RL algorithms like PPO and GRPO, the research found that scaling environment diversity yields the most significant improvements, followed by task diversity, and then trajectory diversity. The findings suggest that prioritizing environment diversity, particularly world diversity, is crucial for effective agentic RL. AI

IMPACT Provides a framework for optimizing data synthesis in agentic RL, potentially leading to more efficient training of AI agents.

RANK_REASON Research paper published on arXiv detailing new findings in agentic reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Agentic Reinforcement Learning
• arXiv
• Bowei He
• Chinchilla
• Grpo
• Proximal Policy Optimization

• paper
• other

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