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Arxiv
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ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation
- Reinforcement Learning from Human Feedback (RLHF) is a pivotal technique for empowering large language model (LLM) applications.
- The RLHF training process for LLMs requires sophisticated parallelization strategies to improve training efficiency.
- To address this, a novel technique called parameter ReaLlocation is proposed, which dynamically adapts parallelization strategies during training.
- The ReaL system achieves significant speedups and performance improvement compared to baseline methods for RLHF training.
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