{"id":"https://openalex.org/W7164929858","doi":"https://doi.org/10.48550/arxiv.2606.15216","title":"Spokes: Optimizing for Diverse Pretraining Data Selection","display_name":"Spokes: Optimizing for Diverse Pretraining Data Selection","publication_year":2026,"publication_date":"2026-06-13","ids":{"openalex":"https://openalex.org/W7164929858","doi":"https://doi.org/10.48550/arxiv.2606.15216"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.15216","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15216","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.15216","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138739076","display_name":"Clarence Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Clarence","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138750132","display_name":"Yejin Choi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Yejin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138695876","display_name":"Luke Zettlemoyer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zettlemoyer, Luke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138713017","display_name":"Pang Wei Koh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koh, Pang Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5017326180","display_name":"Hai Leong Chieu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chieu, Hai Leong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.8267999887466431,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.8267999887466431,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.08649999648332596,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.024399999529123306,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.730400025844574},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6069999933242798},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4839000105857849},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.4675999879837036},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.45489999651908875},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.37299999594688416},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.3596000075340271}],"concepts":[{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.730400025844574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6693999767303467},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6069999933242798},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4839000105857849},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.4675999879837036},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.45489999651908875},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44609999656677246},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39809998869895935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39329999685287476},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.37299999594688416},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3596000075340271},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C180916674","wikidata":"https://www.wikidata.org/wiki/Q3711935","display_name":"Diversification (marketing strategy)","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C32587265","wikidata":"https://www.wikidata.org/wiki/Q1182260","display_name":"Data deduplication","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C7545210","wikidata":"https://www.wikidata.org/wiki/Q838123","display_name":"Data redundancy","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.15216","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15216","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.15216","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15216","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diversity":[0],"plays":[1],"a":[2,29,44,71,101,108],"critical":[3],"role":[4],"in":[5,104],"data":[6,12,37],"selection,":[7],"improving":[8],"performance":[9,131],"under":[10],"fixed":[11],"budgets":[13],"by":[14,69,132],"reducing":[15],"redundancy":[16],"and":[17,117,134,142,152,163,168,176],"repetition.":[18],"However,":[19],"optimizing":[20,148],"for":[21,149],"diversity":[22,68,153],"is":[23,28],"inherently":[24],"challenging,":[25],"as":[26],"it":[27,120],"set-level":[30],"property":[31],"that":[32,89],"depends":[33],"on":[34,50,76,107,115,140,166],"interactions":[35],"between":[36],"points":[38,136,165],"rather":[39],"than":[40,94],"individual":[41],"examples.":[42],"As":[43],"result,":[45],"existing":[46,123],"approaches":[47],"typically":[48],"rely":[49],"proxies":[51],"or":[52],"approximations,":[53],"which":[54],"often":[55],"fail":[56],"to":[57],"ensure":[58],"sufficiently":[59],"diverse":[60,93],"subsets.":[61],"In":[62],"this":[63],"work,":[64],"we":[65],"directly":[66],"optimize":[67],"introducing":[70],"probabilistic":[72],"diversification":[73],"framework":[74],"based":[75],"the":[77,155],"G-Vendi":[78,105],"score,":[79],"optimized":[80],"via":[81,97],"exponentiated":[82],"gradient":[83],"descent.":[84],"Our":[85],"method":[86],"produces":[87],"subsets":[88],"are":[90],"substantially":[91],"more":[92],"those":[95],"obtained":[96],"random":[98,138],"sampling,":[99],"achieving":[100],"+489":[102],"increase":[103],"score":[106],"500k-sample":[109],"subset.":[110],"We":[111],"evaluate":[112],"our":[113],"approach":[114],"FineWeb":[116],"DCLM,":[118],"where":[119],"consistently":[121],"outperforms":[122],"methods.":[124],"Notably,":[125],"SPOKES":[126,158],"(diversity-only)":[127],"improves":[128],"average":[129],"downstream":[130],"+0.4":[133],"+0.5":[135],"over":[137],"sampling":[139],"DCLM":[141,167],"FineWeb,":[143,169],"respectively.":[144],"More":[145],"importantly,":[146],"jointly":[147],"both":[150],"quality":[151,177],"yields":[154],"strongest":[156],"results:":[157],"achieves":[159],"gains":[160],"of":[161],"+1.5":[162],"+1.4":[164],"outperforming":[170],"all":[171],"baselines,":[172],"including":[173],"semantic":[174],"deduplication":[175],"filtering.":[178]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}