Author

Xu Han

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#artificial intelligence Preprint Sep 2025

Concertina: Data-Centric Adaptive Pipeline Parallelism for Efficient Heterogeneous Long-Context LLM Training

This paper proposes DPP, which transforms PP granularity from a static design choice into a workload-adaptive optimization space over packed, split, and hybrid chunks and further introduces a new coupling between heterogeneous pipeline scheduling and gradient checkpointing.

Shiju Wang, Yujie Wang, Fang-Cheng Fu et al. · 0 citations

Preprint Aug 2026

StateFlow: Sequence Pipeline Parallelism for Long-Context Modeling with Linear Recurrence

StateFlow is presented, a sequence pipeline parallelism system for models with linear recurrence that partitions each sequence into chunks and schedules their execution while propagating boundary states and gradients across chunks, thereby reducing activation lifetimes and improving training throughput.

Wenxuan Zhao, Yingfa Chen, Xu Han et al. · 0 citations