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The Art of Scaling Reinforcement Learning Compute for LLMs research paper by Meta, 2025

Meta · Oct 15, 2025 · Training and scaling · 91 citations · 34 upvotes · unverified

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What it shows

A systematic study defines a framework for analyzing and predicting reinforcement learning scaling in large language models, identifying key design choices that affect compute efficiency and proposing a best-practice recipe.

UnverifiedHugging Face's summary; not yet checked by hand.

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DeepSeek LLM: Scaling Open-Source Language Models with LongtermismDeepSeek LLM, an open-source language model project, develops a large dataset and employs SFT and DPO to achieve performance surpassing LLaMA-2 70B and GPT-3.5 in various benchmarks and open-ended evaluations.DeepSeek · Jan 2024 · Unverified855
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About this paper
Authors
Devvrit Khatri, Lovish Madaan, Rishabh Tiwari and 6 more
arXiv
2510.13786 · PDF
Venue
arXiv.org
Citations
91, 12 influential · Semantic Scholar
Upvotes
34 · Hugging Face
Lab
Meta · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
Influential citationsfirst count: 12Sep 25, 2026
Citationsfirst count: 91Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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