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This paper presents the WeChat-YATT (Yet Another Transformer Trainer in WeChat) framework to address the scalability and efficiency challenges of Reinforcement Learning from Human Feedback (RLHF), a leading paradigm for training large-scale language models and multimodal systems. To address the limitations of existing RLHF frameworks, such as scaling complex multimodal workflows and adapting to dynamic workloads, WeChat-YATT introduces a parallel controller programming model and a dynamic batching scheme. The parallel controller enables flexible and efficient orchestration of complex RLHF workflows, while the dynamic batching scheme adaptively partitions computational resources and schedules workloads to reduce hardware idle time and improve GPU utilization. Experimental results demonstrate that WeChat-YATT significantly improves throughput compared to existing state-of-the-art RLHF training frameworks. It has also been successfully deployed to train models supporting WeChat product features, demonstrating its effectiveness and robustness in real-world applications. The source code is publicly available.
Takeaways, Limitations
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Takeaways:
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We present a novel framework, WeChat-YATT, that addresses the scalability and efficiency challenges of complex multimodal RLHF workflows.
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Solving bottlenecks and improving performance of existing RLHF training through a parallel controller programming model and dynamic batching scheme.
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Successfully applied to WeChat products with a large user base, verifying practicality and stability.
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Improving accessibility through open source disclosure
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Limitations:
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Lack of information on the details and reproducibility of the experimental results presented in the paper.
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A more in-depth comparative analysis with other RLHF frameworks is needed.
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Further research is needed on the specific environmental dependence and generalizability of WeChat-YATT.
