ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation

Zhiyu Mei, Wei Fu, Kaiwei Li, Guangju Wang, Huanchen Zhang, Yi Wu

Conference on Machine Learning and Systems 2025 · Day 3 · Session 5: LLM Training and Fine-Tuning

The talk introduces **ReaL (Reinforcement Learning with Parameter Reallocation)**, a novel system designed to significantly enhance the efficiency of **Reinforcement Learning from Human Feedback (RLHF)** for large language models (LLMs). Presented by Zhiyu Mei from Chinghua University and Ant Research, this work addresses the complex computational demands posed by RLHF, which involves multiple interacting models and distinct computational stages, unlike traditional supervised learning. ReaL's core innovation lies in its ability to dynamically manage GPU allocation and parallelization strategies at a fine-grained, task-level during the training process.

AI review

ReaL presents a genuine systems contribution — dynamic GPU allocation and MCMC-based parallelization search for RLHF training — with real benchmark results on 100 H100s at 70B scale. The core idea is sound and the problem framing is honest. But this article reads like an expanded abstract rather than a technical account, and the gaps in reproducibility and implementation specificity keep it from being something an engineer could actually act on without digging into the paper.