This is a page that curates AI-related papers published worldwide. All content here is summarized using Google Gemini and operated on a non-profit basis. Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.
Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms
Created by
Haebom
Author
Jie Xiao, Changyuan Fan, Qingnan Ren, Alfred Long, Yuchen Zhang, Rymon Yu, Eric Yang, Lynn Ai, Shaoduo Gan
Outline
This paper presents a system called Echo to address the serial switching problem between inference and training tasks in reinforcement learning-based post-training of large-scale language models (LLMs). Existing systems perform inference and policy optimization on the same GPU cluster, violating the SPMD assumption. Echo addresses this issue by separating inference and training onto heterogeneous clusters. Two lightweight synchronization protocols (sequential pull mode and asynchronous push-pull mode) are introduced to maximize hardware utilization while maintaining statistical efficiency. Experimental results demonstrate that Echo achieves comparable convergence speed and final rewards to existing methods in geographically distributed clusters using Qwen LLMs of various sizes, while offloading inference tasks to low-cost edge hardware.
Takeaways, Limitations
•
Takeaways:
◦
We demonstrate that separating inference and training tasks can maximize hardware utilization and reduce costs in reinforcement learning training of large-scale language models.
◦
This suggests that data center-class performance can be achieved by leveraging geographically distributed heterogeneous hardware.
◦
We demonstrate that a lightweight synchronization protocol can improve the efficiency of distributed training while maintaining statistical efficiency.
•
Limitations:
◦
The presented experiments are limited to a specific LLM (Qwen) and cluster environment, requiring further research on generalizability.
◦
Further research is needed on its scalability and applicability to LLMs of different sizes and types.
◦
Optimization of the proposed synchronization protocol and study of its adaptability to various environments are needed.
