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

A system called FlexFloor generates approximately 65.40% more response tokens per unit of occupied computing time and raises response generation throughput by 52.74% compared with the verl baseline during reinforcement learning post-training of mixture-of-experts large language models, according to the arXiv paper 2611.06984v1. The researchers report that roughly one-third of occupied worker time in the baseline occurs after local response generation finishes, because workers holding needed experts stay occupied while others process longer responses. FlexFloor addresses this by jointly adapting response scheduling and the active worker group, using historical response lengths and memory constraints to guide batching and prompt placement and progressively releasing workers while maintaining expert availability and correct communication, with little observed variation in rollout reward.

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. FlexFloor jointly adapts response scheduling and the active group of workers. It uses historical response lengths and memory constraints to guide batching and prompt placement, then progressively releases workers while maintaining expert availability and correct communication. Compared with the verl baseline, FlexFloor generates approximately 65.40% more response tokens per unit of occupied computing time and increases response generation throughput by 52.74%, with little observed variation in rollout reward. By making reinforcement learning post-training more efficient, FlexFloor supports faster development and iterative improvement of large language models and AI agents.