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Recently, ChatGPT or InstructGPT like large language models (LLM) has made asignificant impact in the AI world. Many works have attempted to reproduce thecomplex InstructGPT's training pipeline, namely Reinforcement Learning withHuman Feedback (RLHF). However, the mainstream distributed RLHF trainingmethods typically adopt a fixed model placement strategy, referred to as theFlattening strategy. This strategy treats all four interdependent modelsinvolved in RLHF as a single entity, distributing them across all devices andapplying parallelism techniques designed for a single model, regardless of thedifferent workloads inherent to each model. As a result, this strategyexacerbates the generation bottlenecks in the RLHF training and degrades theoverall training efficiency. To address these issues, we propose an adaptivemodel placement framework that offers two flexible model placement strategies.The Interleaving strategy helps reduce memory redundancy and communicationcosts of RLHF training by placing models without dependencies on exclusivedevices with careful orchestration. On the other hand, the Separation strategyimproves the throughput of model training by separating the training andinference runtime of the RLHF pipeline with additional shadow models.Furthermore, our framework provides a simple user interface and allows for theagile allocation of models across devices in a fine-grained manner for varioustraining scenarios, involving models of varying sizes and devices of differentscales. Extensive experiments have demonstrated that our Interleaving andSeparation strategies can achieve notable improvements up to 11X, compared tothe current SOTA approaches. The results highlight the effectiveness andadaptability of our approaches in accelerating the training of distributedRLHF.

An Adaptive Placement and Parallelism Framework for Accelerating RLHF Training