G-Core: A Simple, Scalable and Balanced RLHF Trainer

• URL: http://arxiv.org/abs/2507.22789v2
• Date: Thu, 31 Jul 2025 02:18:13 GMT
• Title: G-Core: A Simple, Scalable and Balanced RLHF Trainer
• Authors: Junyu Wu, Weiming Chang, Xiaotao Liu, Guanyou He, Haoqiang Hong, Boqi Liu, Hongtao Tian, Tao Yang, Yunsheng Shi, Feng Lin, Ting Yao,
• Abstract summary: Reinforcement Learning from Human Feedback (RLHF) has become an increasingly popular paradigm for training large language models.<n>We present textbfG-Core, a simple, scalable, and balanced RLHF training framework designed to address these challenges.
• Score: 35.65011046623611
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning from Human Feedback (RLHF) has become an increasingly popular paradigm for training large language models (LLMs) and diffusion models. While existing RLHF training systems have enabled significant progress, they often face challenges in scaling to multi-modal and diffusion workflows and adapting to dynamic workloads. In particular, current approaches may encounter limitations in controller scalability, flexible resource placement, and efficient orchestration when handling complex RLHF pipelines, especially in scenarios involving dynamic sampling or generative reward modeling. In this paper, we present \textbf{G-Core}, a simple, scalable, and balanced RLHF training framework designed to address these challenges. G-Core introduces a parallel controller programming model, enabling flexible and efficient orchestration of complex RLHF workflows without the bottlenecks of a single centralized controller. Furthermore, we propose a dynamic placement schema that adaptively partitions resources and schedules workloads, significantly reducing hardware idle time and improving utilization, even under highly variable training conditions. G-Core has successfully trained models that support WeChat product features serving a large-scale user base, demonstrating its effectiveness and robustness in real-world scenarios. Our results show that G-Core advances the state of the art in RLHF training, providing a solid foundation for future research and deployment of large-scale, human-aligned models.

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