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Nereus: Adaptive Parallelism for LLM Post-Training

Nereus is a runtime system designed to adapt reinforcement learning post-training for large language models (LLMs) by dynamically adjusting execution plans based on changing conditions. It uses a cost-aware approach to manage GPU resource allocation and model-stage transitions, improving efficiency in distributed training environments.

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PublishedSeptember 28, 2026Songlin Jiang, Tuo Shi, Sitong Zhang
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Nereus can help optimize the performance of LLM post-training by dynamically adjusting execution plans to changing conditions, which can lead to significant improvements in efficiency and resource utilization.

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