Resource-Efficient Reinforcement Learning Post-Training through Elastic Expert Parallelism

arXiv cs.AINovember 6, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2611.06984v1 Announce Type: new Abstract: Reinforcement learning post-training improves the capabilities of large language models, but generating training responses is a major computational bottleneck. In mixture-of-experts models, workers that finish their assigned responses may remain occupied because their experts are still needed by workers processing longer responses. Consequently, approximately one-third of occupied worker time in the baseline occurs after local response generation finishes. Here we present FlexFloor, a system that enables earlier resource release during synchronous response generation while preserving the expert computation required by ongoing responses. FlexFlo

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