{"id":"https://openalex.org/W7133345924","doi":"https://doi.org/10.48550/arxiv.2603.01293","title":"Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models","display_name":"Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7133345924","doi":"https://doi.org/10.48550/arxiv.2603.01293"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01293","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01293","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.2603.01293","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010342958","display_name":"Adel Javanmard","orcid":"https://orcid.org/0000-0003-1934-8747"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Javanmard, Adel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009166220","display_name":"Baharan Mirzasoleiman","orcid":"https://orcid.org/0000-0002-9374-9496"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mirzasoleiman, Baharan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5075598023","display_name":"Vahab Mirrokni","orcid":"https://orcid.org/0000-0001-6705-5629"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mirrokni, Vahab","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/T10028","display_name":"Topic Modeling","score":0.2766000032424927,"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":0.2766000032424927,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.17430000007152557,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.1054999977350235,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7113000154495239},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6960999965667725},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5576000213623047},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.42649999260902405},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4205000102519989},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.4043999910354614},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.38510000705718994},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.34279999136924744}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7113000154495239},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6960999965667725},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6682999730110168},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5992000102996826},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5576000213623047},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5478000044822693},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.42649999260902405},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4205000102519989},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.4043999910354614},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.38510000705718994},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.34200000762939453},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3215000033378601},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.31040000915527344},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2709999978542328}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01293","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01293","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.2603.01293","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01293","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":[{"score":0.4805784225463867,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"are":[4],"pretrained":[5,124,152],"on":[6,33,66,83,142,161],"massive":[7],"datasets":[8,130],"and":[9,58,69,110],"later":[10,106],"instruction-tuned":[11],"via":[12],"supervised":[13],"fine-tuning":[14],"(SFT)":[15],"or":[16],"reinforcement":[17],"learning":[18],"(RL).":[19],"Best":[20],"practices":[21],"emphasize":[22],"large,":[23],"diverse":[24],"pretraining":[25,57,100,134],"data,":[26],"whereas":[27],"post-training":[28],"operates":[29],"differently:":[30],"SFT":[31,64,73,112,129],"relies":[32],"smaller,":[34],"high-quality":[35,72],"datasets,":[36,62],"while":[37,126],"RL":[38,59,138],"benefits":[39],"more":[40],"from":[41,115],"scale,":[42],"with":[43,159],"larger":[44],"amounts":[45],"of":[46,119],"feedback":[47],"often":[48],"outweighing":[49],"label":[50],"quality.":[51],"Yet":[52],"it":[53],"remains":[54],"unclear":[55],"why":[56,63],"require":[60],"large":[61,128,162],"excels":[65],"smaller":[67],"ones,":[68],"what":[70],"defines":[71],"data.":[74],"In":[75,136],"this":[76],"work,":[77],"we":[78],"theoretically":[79],"analyze":[80],"transformers":[81],"trained":[82],"an":[84],"in-context":[85],"weight":[86],"prediction":[87],"task":[88],"for":[89,122,150],"linear":[90],"regression.":[91],"Our":[92],"analysis":[93],"reveals":[94],"several":[95],"key":[96],"findings:":[97],"$(i)$":[98],"balanced":[99],"data":[101,144],"can":[102],"induce":[103],"latent":[104],"capabilities":[105],"activated":[107],"during":[108],"post-training,":[109],"$(ii)$":[111],"learns":[113],"best":[114],"a":[116],"small":[117],"set":[118],"examples":[120],"challenging":[121],"the":[123,151],"model,":[125],"excessively":[127],"may":[131],"dilute":[132],"informative":[133],"signals.":[135],"contrast,":[137],"is":[139,146],"most":[140],"effective":[141],"large-scale":[143],"that":[145],"not":[147],"overly":[148],"difficult":[149],"model.":[153],"We":[154],"validate":[155],"these":[156],"theoretical":[157],"insights":[158],"experiments":[160],"nonlinear":[163],"transformer":[164],"architectures.":[165]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-04T00:00:00"}