Research: Meta paper on The Art of Scaling Reinforcement Learning Compute for LLMs

Researchers from Meta with university researchers from UT Austin, UCL, UC Berkeley, Harvard, along with Periodic Labs, posted on arXiv a paper on scaling in reinforcement learning (instead of pre-training).

One of their key findings is that “(3) Stable, scalable recipes follow predictable scaling trajectories, enabling extrapolation from smaller-scale runs.”

ABSTRACT

EXCERPT

Meta just dropped this paper that spills the secret sauce of reinforcement learning (RL) on LLMs.

It lays out an RL recipe, uses 400,000 GPU hrs and posits a scaling law for performance with more compute in RL, like the classic pretraining scaling laws.

Must read for AI nerds. pic.twitter.com/WH0cnIcIkY
— Deedy (@deedydas) October 17, 2025

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I discuss the importance of understanding scaling in analyzing the fair use defense of AI researchers in my forthcoming article in the Houston Law Review:

My paper on “Fair Use and the Origin of AI Training.” Or why the Copyright Office Report is wrong about fair use and why courts should reject its view.

Chat GPT Is Eating the World

Research: Meta paper on The Art of Scaling Reinforcement Learning Compute for LLMs

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