Kavli Affiliate: Feng Yuan
| First 5 Authors: Zhixin Wang, Zhixin Wang, , ,
| Summary:
Reinforcement learning (RL) has become the pivotal post-training technique
for large language model. Effectively scaling reinforcement learning is now the
key to unlocking advanced reasoning capabilities and ensuring safe,
goal-aligned behavior in the most powerful LLMs. Mainstream frameworks usually
employ a hybrid-controller architecture where a single-controller dispatches
the overall execution logic and manages overall data transfer and the
multi-controller executes distributed computation. For large-scale
reinforcement learning, minor load imbalances can introduce significant
bottlenecks, ultimately constraining the scalability of the system. To address
this limitation, we introduce DistFlow, a novel, fully distributed RL framework
designed to break scaling barrier. We adopt a multi-controller paradigm that
dispatches data transfer and execution tasks to all workers, which eliminates
the centralized node. This allows each worker to operate independently, leading
to near-linear scalability up to thousands of GPUs and dramatic efficiency
gains. Furthermore, our architecture decouples resource configuration from
execution logic, allowing each worker to have a unique execution flow, offering
significant flexibility for rapid and cost-effective algorithmic
experimentation. Extensive experiments show that DistFlow achieves excellent
linear scalability and up to a 7x end-to-end throughput improvement over
state-of-the-art (SOTA) frameworks.
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