Data scaling in agentic reinforcement learning, with synthesis strategies for tasks, environments, and trajectories
Read the original at arxiv.org→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...
Original headline: "How Data Scales in Agentic Reinforcement Learning: Laws and Synthesis Strategies"
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- Oct 20, 04:00 UTC arXiv cs.LG lead source How Data Scales in Agentic Reinforcement Learning: Laws and Synthesis Strategies