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Computing & AI · Source-linked note

WLB-LLM: workload-aware packing and document sharding for 4D training

Balanced packed-document training using profiled compute/communication costs, variable-length micro-batches and delayed outlier documents, alongside per-document context sharding to address pipeline and context imbalance.

What is recorded here?

Balanced packed-document training using profiled compute/communication costs, variable-length micro-batches and delayed outlier documents, alongside per-document context sharding to address pipeline and context imbalance.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

Document attention work is not determined by token count alone. Packing is heuristic under memory and delay constraints; delaying long documents changes sample timing and may affect data randomness. Reported balance and speedups are measured for selected configurations, not an exact globally optimal schedule for every model.

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People and systems

Zheng Wang; Anna Cai; Xinfeng Xie; Zaifeng Pan; Yue Guan; Weiwei Chu; Jie Wang; Shikai Li; Jianyu Huang; Chris Cai; Yuchen Hao; Yufei Ding

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

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English catalogue narrative

WLB-LLM: workload-aware packing and document sharding for 4D training

Balanced packed-document training using profiled compute/communication costs, variable-length micro-batches and delayed outlier documents, alongside per-document context sharding to address pipeline and context imbalance.

Scientific scope

Document attention work is not determined by token count alone. Packing is heuristic under memory and delay constraints; delaying long documents changes sample timing and may affect data randomness. Reported balance and speedups are measured for selected configurations, not an exact globally optimal schedule for every model.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

Zheng Wang; Anna Cai; Xinfeng Xie; Zaifeng Pan; Yue Guan; Weiwei Chu; Jie Wang; Shikai Li; Jianyu Huang; Chris Cai; Yuchen Hao; Yufei Ding

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

Original sources