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Resource-Efficient Reinforcement Learning Post-Training through Elastic Expert Parallelism
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reinforcement learningresource efficiencylarge language modelsparallel processing
2611.06984
Builder Relevance
3h ago80%
Abstract
FlexFloor improves resource efficiency in reinforcement learning post-training for large language models by optimizing worker scheduling and response generation.
Reality Card
Core Claim
FlexFloor increases response generation throughput by 52.74% while generating 65.40% more response tokens per unit of occupied computing time.
Method / Result
Increases response generation throughput by 52.74%.
Limitations
The paper does not specify the authors or provide detailed experimental setups, which may hinder reproducibility.
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