{"id":"https://openalex.org/W7160271086","doi":"https://doi.org/10.48550/arxiv.2605.02105","title":"Sharpness-Aware Pretraining Mitigates Catastrophic Forgetting","display_name":"Sharpness-Aware Pretraining Mitigates Catastrophic Forgetting","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7160271086","doi":"https://doi.org/10.48550/arxiv.2605.02105"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.02105","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02105","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.2605.02105","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135403822","display_name":"Ishaan Watts","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Watts, Ishaan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135286269","display_name":"Catherine Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Catherine","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049130324","display_name":"Sachin Goyal","orcid":"https://orcid.org/0000-0002-6490-7555"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Goyal, Sachin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035773495","display_name":"Jacob M. Springer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Springer, Jacob Mitchell","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135359687","display_name":"Aditi Raghunathan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raghunathan, Aditi","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/T10036","display_name":"Advanced Neural Network Applications","score":0.4763000011444092,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.4763000011444092,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.1290999948978424,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.030799999833106995,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/forgetting","display_name":"Forgetting","score":0.8679999709129333},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.6037999987602234},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.414900004863739},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4027000069618225},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.3321000039577484},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.32330000400543213}],"concepts":[{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.8679999709129333},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.6037999987602234},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5249000191688538},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.414900004863739},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4027000069618225},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.39340001344680786},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.358599990606308},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3424000144004822},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.3321000039577484},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.25589999556541443}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.02105","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02105","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.2605.02105","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02105","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":{"Pretraining":[0],"optimizers":[1],"are":[2],"tuned":[3],"to":[4,81,101,116],"produce":[5],"the":[6,12,32,35,43],"strongest":[7],"possible":[8],"base":[9,36,44],"model,":[10],"on":[11,95],"assumption":[13],"that":[14,57,86],"a":[15,20,110],"stronger":[16,21],"starting":[17],"point":[18],"yields":[19],"model":[22,37,76],"after":[23,93,125,131],"subsequent":[24,48],"changes":[25],"like":[26],"post-training":[27,94,127],"and":[28,69,128],"quantization.":[29,133],"This":[30],"overlooks":[31],"geometry":[33],"of":[34,42],"which":[38],"controls":[39],"how":[40],"much":[41],"model's":[45],"capabilities":[46],"survive":[47],"parameter":[49],"updates.":[50],"We":[51],"study":[52],"three":[53],"pretraining":[54],"optimization":[55,59],"approaches":[56],"bias":[58],"toward":[60],"flatter":[61],"minima:":[62],"Sharpness-Aware":[63],"Minimization":[64],"(SAM),":[65],"large":[66],"learning":[67,71],"rates,":[68],"shortened":[70],"rate":[72],"annealing":[73],"periods.":[74],"Across":[75],"sizes":[77],"ranging":[78],"from":[79],"20M":[80],"150M":[82],"parameters,":[83],"we":[84],"find":[85],"these":[87],"interventions":[88],"consistently":[89],"improve":[90],"downstream":[91],"performance":[92],"five":[96],"common":[97],"datasets":[98],"with":[99],"up":[100],"80%":[102],"less":[103],"forgetting.":[104],"These":[105],"principles":[106],"hold":[107],"at":[108],"scale:":[109],"short":[111],"SAM":[112],"mid-training":[113],"phase":[114],"applied":[115],"an":[117],"existing":[118],"OLMo-2-1B":[119],"checkpoint":[120],"reduces":[121],"forgetting":[122],"by":[123,129],"31%":[124],"MetaMath":[126],"40%":[130],"4-bit":[132]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-06T00:00:00"}