WeChat-YATT: A Scalable, Simple, Efficient, and Production Ready Training Library

📄 arXiv: 2508.07970v3 📥 PDF

作者: Junyu Wu, Weiming Chang, Xiaotao Liu, Guanyou He, Tingfeng Xian, Haoqiang Hong, Boqi Chen, Hongtao Tian, Tao Yang, Yunsheng Shi, Feng Lin, Ting Yao, Jiatao Xu

分类: cs.LG, cs.AI

发布日期: 2025-08-11 (更新: 2025-08-18)

备注: arXiv admin note: substantial text overlap with arXiv:2507.22789

💡 一句话要点

提出WeChat-YATT以解决多模态RLHF训练的可扩展性问题

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 人类反馈强化学习 多模态系统 训练框架 资源分配 可扩展性 GPU利用率 WeChat产品

📋 核心要点

  1. 现有RLHF训练框架在处理复杂多模态工作流和动态工作负载时面临可扩展性和效率的挑战。
  2. WeChat-YATT通过并行控制器编程模型和动态资源分配方案，提供灵活高效的RLHF训练解决方案。
  3. 实验结果表明，WeChat-YATT在吞吐量上显著优于现有框架，且已成功应用于WeChat的实际产品中。

📝 摘要（中文）

人类反馈强化学习（RLHF）已成为训练大型语言模型和多模态系统的重要范式。尽管现有RLHF训练框架取得了显著进展，但在处理复杂多模态工作流和动态工作负载时仍面临挑战。当前系统在管理大型模型时常遇到控制器可扩展性限制，以及在复杂RLHF管道的协调上效率低下。为此，本文提出了WeChat-YATT（Yet Another Transformer Trainer），这是一个简单、可扩展且平衡的RLHF训练框架，旨在解决这些问题。WeChat-YATT采用并行控制器编程模型，灵活高效地协调复杂的RLHF工作流，缓解了集中控制器架构的瓶颈，并在大规模数据场景中促进了可扩展性。此外，我们提出了一种动态资源分配方案，能够自适应地划分计算资源和调度工作负载，从而显著减少硬件闲置时间，提高GPU利用率。通过多种实验场景的评估，WeChat-YATT在吞吐量上显著优于现有的RLHF训练框架，并成功应用于支持WeChat产品功能的模型训练，证明了其在实际应用中的有效性和鲁棒性。

🔬 方法详解

问题定义：本文旨在解决现有RLHF训练框架在处理复杂多模态工作流时的可扩展性和效率问题，尤其是在动态采样和资源分配方面的不足。

核心思路：WeChat-YATT的核心思路是采用并行控制器编程模型，以实现灵活的工作流协调，并通过动态资源分配来优化计算资源的利用率。这样的设计能够有效缓解集中控制器架构带来的瓶颈。

技术框架：WeChat-YATT的整体架构包括多个模块：并行控制器、动态资源分配模块和RLHF工作流管理模块。并行控制器负责协调各个子任务的执行，而动态资源分配模块则根据实时需求调整计算资源的分配。

关键创新：WeChat-YATT的主要创新在于其并行控制器编程模型和动态资源分配方案，这与传统的集中控制器架构形成了鲜明对比，显著提高了系统的可扩展性和效率。

关键设计：在关键设计方面，WeChat-YATT采用了自适应的计算资源划分策略，并优化了GPU利用率，减少了硬件闲置时间。具体的参数设置和损失函数设计尚未详细披露，属于未知领域。

📊 实验亮点

实验结果显示，WeChat-YATT在多个场景下的吞吐量显著提高，相较于最先进的RLHF训练框架，提升幅度达到XX%（具体数据待补充）。这一成果验证了其在实际应用中的有效性和鲁棒性。

🎯 应用场景

WeChat-YATT的潜在应用领域包括大型语言模型和多模态系统的训练，特别是在需要动态调整资源和高效处理复杂工作流的场景中。其实际价值在于提升了训练效率和模型性能，未来可能对多种AI应用产生深远影响。

📄 摘要（原文）

Reinforcement Learning from Human Feedback (RLHF) has emerged as a prominent paradigm for training large language models and multimodal systems. Despite the notable advances enabled by existing RLHF training frameworks, significant challenges remain to scale to complex multimodal workflows and adapt to dynamic workloads. In particular, current systems often encounter limitations related to controller scalability when managing large models, as well as inefficiencies in orchestrating intricate RLHF pipelines, especially in scenarios that require dynamic sampling and resource allocation. In this paper, we introduce WeChat-YATT Yet Another Transformer Trainer in WeChat, a simple, scalable, and balanced RLHF training framework specifically designed to address these challenges. WeChat-YATT features a parallel controller programming model that enables flexible and efficient orchestration of complex RLHF workflows, effectively mitigating bottlenecks associated with centralized controller architectures and facilitating scalability in large-scale data scenarios. In addition, we propose a dynamic placement schema that adaptively partitions computational resources and schedules workloads, thereby significantly reducing hardware idle time and improving GPU utilization under variable training conditions. We evaluate WeChat-YATT across diverse experimental scenarios, demonstrating its substantial throughput improvements over state-of-the-art RLHF training frameworks. Furthermore, WeChat-YATT has been successfully deployed to train models that support WeChat product features for a large-scale user base, underscoring its effectiveness and robustness in real-world applications. We have made WeChat-YATT publicly available at https://www.github.com/tencent/WeChat-YATT.