Mixtera: A Data Plane for Foundation Model Training

Summary: Mixtera: declarative data plane for foundation-model training, expressing sample mixtures and visitation order over arbitrary data properties atop existing collections. Centralized read-only layer supports dynamic, feedback-driven reweighting; scales to 256 GH200, no training bottleneck, and implements ADO. (summarized by gpt-5-mini on Apr 11 2026)

Paper ID: 7494
Venue: SIGMOD
Year: 2026
Pagerank: 4.1945683e-05
Overall Rank: 10,183 | 29.16%
DOI: 10.1145/3786668

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
185	DuckDB: an Embeddable Analytical Database	2019	SIGMOD	0.00036538405
538	The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing	2015	VLDB	0.00020678804
1,504	Analyzing and Mitigating Data Stalls in DNN Training	2021	VLDB	0.00011642333
2,170	tf.data: A Machine Learning Data Processing Framework	2021	VLDB	9.3821603e-05
2,902	PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel	2023	VLDB	7.93939e-05
5,921	Data-Juicer: A One-Stop Data Processing System for Large Language Models	2024	SIGMOD	5.2725159e-05
8,735	TensorSocket: Shared Data Loading for Deep Learning Training	2026	SIGMOD	4.456315e-05
8,736	Unveiling Challenges for LLMs in Enterprise Data Engineering	2026	VLDB	4.456315e-05
8,737	Scheduling Data Processing Pipelines for Incremental Training on MLP-based Recommendation Models	2025	SIGMOD	4.456315e-05

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