SeamlessFlow: A Trainer Agent Isolation RL Framework Achieving Bubble-Free Pipelines via Tag Scheduling

2025-08-15

AI Summary

This paper addresses two key challenges in industrial-scale reinforcement learning (RL): tight coupling between training and execution, and low GPU utilization with poor system scalability. To tackle these, we propose a decoupled, serverless RL framework. Our method separates the trainer and agent execution pipelines, introduces a data plane and trajectory management mechanism to enable seamless pausing and resuming of rollouts; designs a label-driven scheduler and spatiotemporal multiplexing pipeline to eliminate pipeline bubbles and unify heterogeneous resource scheduling; and dynamically reassigns idle training nodes to rollout tasks. Experimental results demonstrate that our framework significantly improves GPU utilization, reduces system idle time, and achieves high throughput, strong stability, and excellent scalability—particularly in complex scenarios such as multi-agent and long-horizon RL tasks.

Technology Category

• Machine Learning: Reinforcement Learning
• Planning, Routing, and Scheduling: Planning with Language Models
• Multiagent Systems: Multiagent Learning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We introduce SeamlessFlow, a server based reinforcement learning (RL) framework that addresses two core challenges in industrial scale RL: (1) decoupling RL training from the complex execution flow of agents; (2) maximizing GPU utilization with minimal idle time while preserving the stability and scalability required for large-scale deployments. First, SeamlessFlow introduces a data plane that decouples the RL trainer from diverse, complex agent implementations while sustaining high throughput. A central trajectory manager maintains complete interaction histories and supports partial rollout, allowing rollout to pause for weight updates and resume seamlessly, keeping agents unaware of service interruptions. Second, we propose a tag driven scheduling paradigm that abstracts hardware into capability tagged resources, unifying colocated and disaggregated architectures. Based on this, SeamlessFlow introduces a spatiotemporal multiplexing pipeline that dynamically reassigns idle training nodes to rollout in a train rollout separated setup, eliminating pipeline bubbles and fully exploiting heterogeneous cluster resources. By combining these innovations, SeamlessFlow delivers both stability and high performance, making it well suited for multi agent, long horizon, and other complex RL tasks.

Problem

• Decouples RL training from complex agent execution flow
• Maximizes GPU utilization with minimal idle time
• Eliminates pipeline bubbles in heterogeneous cluster resources

Innovation

• Decouples RL training from agent execution
• Uses tag driven scheduling for hardware abstraction
• Implements spatiotemporal multiplexing for GPU utilization