Efficient GPU Memory Management for Large-Scale RL Training

Develop a scalable GPU memory management method for large-scale reinforcement learning (RL) of large language models that efficiently manages model states, activations, and experience data throughout the training cycle without introducing significant overhead.

Background

The paper reviews infrastructure challenges for training large-scale reinforcement learning models and highlights memory efficiency as a core unsolved issue. Existing training and inference frameworks (e.g., vLLM, SGLang, Megatron-LM, VeRL, OpenRLHF) typically keep model states and communication groups resident in GPU memory, leading to static and inefficient usage.

The authors note that current libraries (e.g., NCCL) lack native support for live memory offloading and that ad hoc approaches like destroying and recreating communication groups incur prohibitive re-initialization costs at scale. While the paper introduces ASystem and its AMem component to mitigate memory bottlenecks, the general problem of managing massive GPU memory footprints without overhead is explicitly marked as open.