What is recorded here?
Fixed training recipes and evaluation tasks around a240T-token candidate pool, enabling controlled experiments in filtering, deduplication and mixing, with DCLM-Baseline and released language models.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The candidate pool is not a claim that every baseline trained on240T tokens. Comparisons are for specified412M–7B scales, compute budgets and evaluation protocols; no universally optimal data filter or corpus-quality theorem. Dataset design and OpenLM/benchmark inheritance remain distinct.
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People and systems
Jeffrey Li; Alex Fang; Georgios Smyrnis; Maor Ivgi; Matt Jordan; Samir Gadre; Hritik Bansal; Etash Guha; Sedrick Keh; Kushal Arora; Saurabh Garg; Rui Xin; Niklas Muenninghoff; Reinhard Heckel; Jean Mercat; Mayee Chen; Suchin Gururangan; Mitchell Wortsman; Alon Albalak; Yonatan Bitton; Marianna Nezhurina; Amro Abbas; Cheng-Yu Hsieh; Dhruba Ghosh; Josh Gardner; Maciej Kilian; Hanlin Zhang; Rulin Shao; Sarah Pratt; Sunny Sanyal; Gabriel Ilharco; Giannis Daras; Kalyani Marathe; Aaron Gokaslan; Jieyu Zhang; Khyathi Chandu; Thao Nguyen; Igor Vasiljevic; Sham Kakade; Shuran Song; Sujay Sanghavi; Fartash Faghri; Sewoong Oh; Luke Zettlemoyer; Kyle Lo; Alaaeldin El-Nouby; Hadi Pouransari; Alexander Toshev; Stephanie Wang; Dirk Groeneveld; Luca Soldani; Pang Wei Koh; Jenia Jitsev; Thomas Kollar; Alexandros G. Dimakis; Yair Carmon; Achal Dave; Ludwig Schmidt; Vaishaal Shankar
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
DataComp-LM: controlled language-model data-curation testbed
Fixed training recipes and evaluation tasks around a240T-token candidate pool, enabling controlled experiments in filtering, deduplication and mixing, with DCLM-Baseline and released language models.
Scientific scope
The candidate pool is not a claim that every baseline trained on240T tokens. Comparisons are for specified412M–7B scales, compute budgets and evaluation protocols; no universally optimal data filter or corpus-quality theorem. Dataset design and OpenLM/benchmark inheritance remain distinct.
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
Jeffrey Li; Alex Fang; Georgios Smyrnis; Maor Ivgi; Matt Jordan; Samir Gadre; Hritik Bansal; Etash Guha; Sedrick Keh; Kushal Arora; Saurabh Garg; Rui Xin; Niklas Muenninghoff; Reinhard Heckel; Jean Mercat; Mayee Chen; Suchin Gururangan; Mitchell Wortsman; Alon Albalak; Yonatan Bitton; Marianna Nezhurina; Amro Abbas; Cheng-Yu Hsieh; Dhruba Ghosh; Josh Gardner; Maciej Kilian; Hanlin Zhang; Rulin Shao; Sarah Pratt; Sunny Sanyal; Gabriel Ilharco; Giannis Daras; Kalyani Marathe; Aaron Gokaslan; Jieyu Zhang; Khyathi Chandu; Thao Nguyen; Igor Vasiljevic; Sham Kakade; Shuran Song; Sujay Sanghavi; Fartash Faghri; Sewoong Oh; Luke Zettlemoyer; Kyle Lo; Alaaeldin El-Nouby; Hadi Pouransari; Alexander Toshev; Stephanie Wang; Dirk Groeneveld; Luca Soldani; Pang Wei Koh; Jenia Jitsev; Thomas Kollar; Alexandros G. Dimakis; Yair Carmon; Achal Dave; Ludwig Schmidt; Vaishaal Shankar
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.