DynaResize: Runtime GPU Reallocation for Disaggregated LLM Post-Training

Abstract:RL-based LLM post-training increasingly disaggregates Rollout and Training across separate GPU resources, but static GPU partitioning suffers from severe pipeline bubbles under long-tail rollout latency. We present DynaResize, a runtime GPU reallocation system that dynamically switches GPUs between Rollout and Training to balance stage execution times without changing RL semantics. DynaResize decomposes resizing into fine-grained operations and removes non-startup-critical work from the critical path through communicator reuse, bounded state staging, and hysteresis-based resizing. Experimental results show that DynaResize can improve end-to-end throughput by 66.5% and reduce total execution time by 33% over the optimal static configuration, while hiding 27% of role-switching overhead.

Artificial Intelligence (cs.AI)

arXiv:2607.22614 [cs.AI]

(or arXiv:2607.22614v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.22614 Focus to learn more arXiv-issued DOI via DataCite

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