{"id":"https://openalex.org/W7128390083","doi":"https://doi.org/10.48550/arxiv.2602.06394","title":"Unlocking Noisy Real-World Corpora for Foundation Model Pre-Training via Quality-Aware Tokenization","display_name":"Unlocking Noisy Real-World Corpora for Foundation Model Pre-Training via Quality-Aware Tokenization","publication_year":2026,"publication_date":"2026-02-06","ids":{"openalex":"https://openalex.org/W7128390083","doi":"https://doi.org/10.48550/arxiv.2602.06394"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.06394","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052807455","display_name":"Arvid Gollwitzer","orcid":"https://orcid.org/0009-0001-2170-8089"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gollwitzer, Arvid E.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055386344","display_name":"Paridhi Latawa","orcid":"https://orcid.org/0000-0001-6670-5546"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Latawa, Paridhi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125379849","display_name":"David de Gruijl","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"de Gruijl, David","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125403763","display_name":"Deepak A. Subramanian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Subramanian, Deepak A.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5119282926","display_name":"Adri\u00e1n Noriega de la Colina","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"de la Colina, Adri\u00e1n Noriega","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5052807455"],"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/T10028","display_name":"Topic Modeling","score":0.2093999981880188,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.2093999981880188,"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/T11642","display_name":"Genomics and Rare Diseases","score":0.1071000024676323,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12090","display_name":"Language and cultural evolution","score":0.09459999948740005,"subfield":{"id":"https://openalex.org/subfields/3316","display_name":"Cultural Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lexical-analysis","display_name":"Lexical analysis","score":0.6883000135421753},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6664999723434448},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4864000082015991},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4287000000476837},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.40130001306533813},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.36559998989105225},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.33329999446868896}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7731000185012817},{"id":"https://openalex.org/C176982825","wikidata":"https://www.wikidata.org/wiki/Q835922","display_name":"Lexical analysis","level":2,"score":0.6883000135421753},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6664999723434448},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5576000213623047},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4864000082015991},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4542999863624573},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4287000000476837},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.33329999446868896},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33309999108314514},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2874000072479248},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2630999982357025},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2621000111103058},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.2567000091075897}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.06394","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.06394","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.06394","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.06394","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"Current":[0],"tokenization":[1],"methods":[2],"process":[3],"sequential":[4],"data":[5,25],"without":[6],"accounting":[7],"for":[8,74,140],"signal":[9],"quality,":[10],"limiting":[11],"their":[12],"effectiveness":[13],"on":[14],"noisy":[15,126],"real-world":[16,127],"corpora.":[17],"We":[18,31,124],"present":[19],"QA-Token":[20],"(Quality-Aware":[21],"Tokenization),":[22],"which":[23],"incorporates":[24],"reliability":[26],"directly":[27],"into":[28],"vocabulary":[29,44],"construction.":[30],"make":[32],"three":[33],"key":[34],"contributions:":[35],"(i)":[36],"a":[37,50,104],"bilevel":[38],"optimization":[39],"formulation":[40],"that":[41,54],"jointly":[42],"optimizes":[43],"construction":[45],"and":[46,64,111,134],"downstream":[47],"performance,":[48],"(ii)":[49],"reinforcement":[51],"learning":[52,69],"approach":[53],"learns":[55],"merge":[56],"policies":[57],"through":[58],"quality-aware":[59],"rewards":[60],"with":[61,144],"convergence":[62],"guarantees,":[63],"(iii)":[65],"an":[66],"adaptive":[67],"parameter":[68],"mechanism":[70],"via":[71],"Gumbel-Softmax":[72],"relaxation":[73],"end-to-end":[75],"optimization.":[76],"Our":[77],"experimental":[78],"evaluation":[79],"demonstrates":[80],"consistent":[81],"improvements:":[82],"genomics":[83],"(6.7":[84],"percentage":[85],"point":[86],"F1":[87],"gain":[88],"in":[89],"variant":[90],"calling":[91],"over":[92],"BPE),":[93],"finance":[94],"(30%":[95],"Sharpe":[96],"ratio":[97],"improvement).":[98],"At":[99],"foundation":[100,141],"scale,":[101],"we":[102],"tokenize":[103],"pretraining":[105],"corpus":[106],"comprising":[107],"1.7":[108],"trillion":[109],"base-pairs":[110],"achieve":[112],"state-of-the-art":[113],"pathogen":[114],"detection":[115],"(94.53":[116],"MCC)":[117],"while":[118],"reducing":[119],"token":[120],"count":[121],"by":[122],"15%.":[123],"unlock":[125],"corpora,":[128],"spanning":[129],"petabases":[130],"of":[131,136],"genomic":[132],"sequences":[133],"terabytes":[135],"financial":[137],"time":[138],"series,":[139],"model":[142],"training":[143],"zero":[145],"inference":[146],"overhead.":[147]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-10T00:00:00"}