{"id":"https://openalex.org/W7167618024","doi":"https://doi.org/10.48550/arxiv.2607.04969","title":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","display_name":"Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training","publication_year":2026,"publication_date":"2026-07-06","ids":{"openalex":"https://openalex.org/W7167618024","doi":"https://doi.org/10.48550/arxiv.2607.04969"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.04969","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.04969","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.04969","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029177503","display_name":"Jingwei Zuo","orcid":"https://orcid.org/0000-0002-3251-6939"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zuo, Jingwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140222457","display_name":"Cong Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Cong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109819325","display_name":"Ilyas Chahed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chahed, Ilyas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071370499","display_name":"Maksim Velikanov","orcid":"https://orcid.org/0000-0001-9428-3934"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Velikanov, Maksim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109819324","display_name":"Dhia Eddine Rhaiem","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rhaiem, Dhia Eddine","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018516907","display_name":"Pasquale Balsebre","orcid":"https://orcid.org/0009-0004-9454-2704"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Balsebre, Pasquale","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140211195","display_name":"Abhay Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Abhay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140169680","display_name":"Younes Belkada","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Belkada, Younes","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140217250","display_name":"Hakim Hacid","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hacid, Hakim","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":0.4120999872684479,"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":0.4120999872684479,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.24469999969005585,"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":0.03830000013113022,"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/reuse","display_name":"Reuse","score":0.7961999773979187},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7754999995231628},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5982000231742859},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.47850000858306885},{"id":"https://openalex.org/keywords/repetition","display_name":"Repetition (rhetorical device)","score":0.4138000011444092},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.3944999873638153}],"concepts":[{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.7961999773979187},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7754999995231628},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7121999859809875},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5982000231742859},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.47850000858306885},{"id":"https://openalex.org/C2776141515","wikidata":"https://www.wikidata.org/wiki/Q1274479","display_name":"Repetition (rhetorical device)","level":2,"score":0.4138000011444092},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.3944999873638153},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.38609999418258667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34540000557899475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30160000920295715},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.2667999863624573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.04969","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.04969","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.04969","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.04969","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"The":[0],"training":[1,12,82,101,147,157],"paradigm":[2,83],"of":[3,19,37,62,100,106],"large":[4],"language":[5],"models":[6],"has":[7],"shifted":[8],"from":[9,68],"traditional":[10],"one-pass":[11],"to":[13,46,119,150],"multi-epoch":[14],"training,":[15],"as":[16,52],"reasonable":[17],"reuse":[18,47,152],"limited":[20,164],"high-quality":[21,165],"data":[22,48,90,107],"can":[23],"improve":[24,120],"both":[25],"model":[26],"performance":[27,117],"and":[28,39,44,72,88,103,154],"sample":[29],"efficiency.":[30],"Meanwhile,":[31],"excessive":[32],"repetition":[33,122],"introduces":[34],"the":[35,98,104,128],"risk":[36],"overfitting":[38],"diminishing":[40],"returns.":[41],"Determining":[42],"when":[43,87],"how":[45,89],"effectively":[49],"thus":[50],"emerges":[51],"a":[53,59,81,113,134,143],"natural":[54],"but":[55],"under-explored":[56],"question.":[57],"Through":[58],"novel":[60],"observation":[61],"model's":[63],"\"Memorization":[64],"Window\"":[65],"signals":[66],"derived":[67],"loss":[69],"retention":[70],"dynamics":[71],"downstream":[73],"evaluation":[74],"scores,":[75],"we":[76],"propose":[77],"\"Memorization-guided":[78],"Data":[79],"Reuse\",":[80],"that":[84],"adaptively":[85],"determines":[86],"should":[91],"be":[92],"reused,":[93],"enabling":[94],"principled":[95],"decisions":[96],"on":[97],"number":[99],"epochs":[102],"scheduling":[105],"replays.":[108],"Our":[109],"preliminary":[110],"experiments":[111],"reveal":[112],"consistent":[114],"memorization-driven":[115],"regime:":[116],"continues":[118],"with":[121,163],"far":[123],"beyond":[124],"current":[125],"practice":[126],"(e.g.,":[127],"commonly":[129],"cited":[130],"four-epoch":[131],"limit).":[132],"While":[133],"full":[135],"scheduler":[136],"remains":[137],"future":[138],"work,":[139],"these":[140],"insights":[141],"provide":[142],"foundation":[144],"for":[145],"memorization-aware":[146],"schedules,":[148],"helping":[149],"determine":[151],"budgets":[153],"move":[155],"toward":[156],"LLMs":[158],"smarter":[159],"rather":[160],"than":[161],"longer":[162],"data.":[166]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-08T00:00:00"}
