The Art of Scaling Reinforcement Learning Compute for LLMs

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Principled framework for scaling RL compute in LLMs

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Open Source

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About The Art of Scaling Reinforcement Learning Compute for LLMs

This research paper presents the first large-scale systematic study, using over 400,000 GPU-hours, to define a principled framework for analyzing and predicting reinforcement learning (RL) scaling in large language models (LLMs). The authors fit sigmoidal compute-performance curves for RL training and ablate various design choices, observing that details such as loss aggregation, normalization, curriculum, and off-policy algorithms primarily affect compute efficiency without shifting asymptotic performance. Based on these insights, they propose a best-practice recipe called ScaleRL and demonstrate its effectiveness by successfully scaling and predicting validation performance on a single RL run up to 100,000 GPU-hours. The work provides both a scientific framework for analyzing scaling in RL and a practical recipe that brings RL training closer to the predictability achieved in pre-training.

Key Features

First large-scale systematic study on RL scaling in LLMs (400,000+ GPU-hours)

Fits sigmoidal compute-performance curves for RL training

Ablates design choices: loss aggregation, normalization, curriculum, off-policy algorithms

Proposes ScaleRL best-practice recipe for stable and predictable scaling

Demonstrates scaling and prediction of validation performance at 100,000 GPU-hours

Provides a scientific framework for analyzing scaling in RL

Pros & Cons

Pros

Based on extensive empirical evidence (400,000+ GPU-hours)
Provides predictive scaling curves for RL training
Identifies which design choices affect compute efficiency vs. asymptotic performance
Offers a practical, best-practice recipe (ScaleRL) validated at large scale
Brings RL training closer to the predictability of pre-training scaling

Cons

Results are based on specific experimental setups and may not generalize to all LLM RL scenarios
Code implementation is not directly provided in the paper (links to code finders but no standalone repository)
The paper focuses on validation performance, not downstream task performance

Best For

Research into scaling reinforcement learning for large language modelsOptimizing compute budgets for RL training of LLMsEvaluating algorithmic improvements for RL scalingExtrapolating small-scale RL experiments to large-scale runs

FAQ

What is ScaleRL?

ScaleRL is a best-practice recipe proposed in the paper for scaling reinforcement learning compute in LLMs, derived from a large-scale systematic study of design choices.

How many GPU-hours were used in the study?

The study used more than 400,000 GPU-hours, with a single RL run scaled up to 100,000 GPU-hours.

What are the main findings of the paper?

The paper finds that not all RL recipes yield similar asymptotic performance; details like loss aggregation, normalization, curriculum, and off-policy algorithms primarily modulate compute efficiency; and stable scalable recipes follow predictable scaling trajectories.