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HOTHF Daily PapersSep 28, 04:00Global๐Ÿค– AI

Nereus: Adaptive Parallelism for LLM Post-Training ๐ŸŒŸ

New approach optimizes RL post-training for large language models on GPU clusters

LLMsRL Post-TrainingGPU ClustersAdaptive Parallelism
Nereus: Adaptive Parallelism for LLM Post-Training ๐ŸŒŸ
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Adaptive Parallelism for LLMs

Nereus addresses challenges in reinforcement learning (RL) post-training for large language models (LLMs) on GPU clusters.

It manages multiple models across generations, inference, and training, adapting to changing factors like resource availability and sequence length.

Dynamic Job Adaptation

Adapting jobs with shared GPU usage poses challenges, including transition costs and coordination of GPU transfers.

Nereus introduces a cost-aware runtime that adjusts RL post-training jobs into efficient execution plans.

Memory-Friendly Planning

Nereus' low-overhead controller selects memory-friendly global plans, admitting transitions using a cost model.

This approach ensures efficient execution and resource utilization during the training process.

โ€œ'Nereus introduces a cost-aware runtime that adapts RL post-training jobs into efficient execution plans.'โ€

โ€” Songlin Jiang, Tuo Shi
TAKEAWAYNereus optimizes LLM post-training efficiency on GPU clusters.
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By Chaos Lab ยท ๅฆ™็ญ”ๆ˜Ÿ็ƒAI