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How Data Scales in Agentic Reinforcement Learning: Laws and Synthesis Strategies

arXiv:2610.23946v1 Announce Type: new Abstract: Pretraining scaling laws treat training data as a one-dimensional quantity: a token count. Agentic reinforcement learning (RL) inherits this scalar abstraction, but modern synthesis pipelines now generate \textit{tasks}, \textit{environments}, and \te…

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  1. 2026-10-20 04:00 · arXiv cs.LG
    How Data Scales in Agentic Reinforcement Learning: Laws and Synthesis Strategies

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