{"id":"https://openalex.org/W7160994919","doi":"https://doi.org/10.48550/arxiv.2605.12484","title":"Learning, Fast and Slow: Towards LLMs That Adapt Continually","display_name":"Learning, Fast and Slow: Towards LLMs That Adapt Continually","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7160994919","doi":"https://doi.org/10.48550/arxiv.2605.12484"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.12484","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12484","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.12484","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136081181","display_name":"Rishabh Tiwari","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tiwari, Rishabh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120338260","display_name":"Kusha Sareen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sareen, Kusha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136040333","display_name":"Lakshya A Agrawal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agrawal, Lakshya A","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136010914","display_name":"Joseph E. Gonzalez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gonzalez, Joseph E.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136065066","display_name":"Matei Zaharia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zaharia, Matei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136028504","display_name":"Kurt Keutzer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Keutzer, Kurt","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063459703","display_name":"Inderjit S. Dhillon","orcid":"https://orcid.org/0000-0002-2759-1416"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dhillon, Inderjit S","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136014214","display_name":"Rishabh Agarwal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agarwal, Rishabh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5077706355","display_name":"Devvrit Khatri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khatri, Devvrit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6700000166893005,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6700000166893005,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.07090000063180923,"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/T10028","display_name":"Topic Modeling","score":0.06459999829530716,"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/forgetting","display_name":"Forgetting","score":0.8719000220298767},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7767999768257141},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6509000062942505},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.40209999680519104},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.3756999969482422}],"concepts":[{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.8719000220298767},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7767999768257141},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6509000062942505},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6442000269889832},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5730000138282776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42010000348091125},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.40209999680519104},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.3756999969482422},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.37279999256134033},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.28780001401901245},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2827000021934509},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.12484","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12484","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.12484","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12484","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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,173,206],"(LLMs)":[3],"are":[4],"trained":[5,205,216],"for":[6,73,104],"downstream":[7],"tasks":[8],"by":[9,56],"updating":[10,17,65],"their":[11],"parameters":[12,18,42,108],"(e.g.,":[13,51,90],"via":[14],"RL).":[15],"However,":[16],"forces":[19],"them":[20],"to":[21,48,76,126,135,138,152,176,181,210,231],"absorb":[22,127],"task-specific":[23,49,129],"information,":[24,130],"which":[25],"can":[26,43,121],"result":[27],"in":[28,187],"catastrophic":[29,189],"forgetting":[30,190],"and":[31,45,112,142],"loss":[32],"of":[33],"plasticity.":[34],"In":[35,218],"contrast,":[36],"in-context":[37,78],"learning":[38,75,102,159,220],"with":[39,106],"fixed":[40],"LLM":[41,66,179],"cheaply":[44],"rapidly":[46],"adapt":[47,207],"requirements":[50],"prompt":[52],"optimization),":[53],"but":[54],"cannot":[55],"itself":[57],"typically":[58],"match":[59],"the":[60,128,139,177,227],"performance":[61,169],"gains":[62],"available":[63],"through":[64],"parameters.":[67],"There":[68],"is":[69,150],"no":[70],"good":[71],"reason":[72],"restricting":[74],"being":[77],"or":[79],"in-weights.":[80],"Moreover,":[81,171],"humans":[82],"also":[83,196],"likely":[84],"learn":[85,122],"at":[86],"different":[87],"time":[88],"scales":[89],"System":[91],"1":[92],"vs":[93],"2).":[94],"To":[95],"this":[96],"end,":[97],"we":[98],"introduce":[99],"a":[100,167,211],"fast-slow":[101],"framework":[103],"LLMs,":[105],"model":[107,141],"as":[109,115],"\"slow\"":[110],"weights":[111,134],"optimized":[113],"context":[114],"\"fast\"":[116],"weights.":[117],"These":[118],"fast":[119],"\"weights\"":[120],"from":[123],"textual":[124],"feedback":[125],"while":[131,164,236],"allowing":[132],"slow":[133,158],"stay":[136],"closer":[137,175],"base":[140,178],"persist":[143],"general":[144],"reasoning":[145,162],"behaviors.":[146],"Fast-Slow":[147],"Training":[148],"(FST)":[149],"up":[151],"3x":[153],"more":[154,208],"sample-efficient":[155],"than":[156,191,214],"only":[157],"(RL)":[160],"across":[161],"tasks,":[163],"consistently":[165],"reaching":[166],"higher":[168],"asymptote.":[170],"FST-trained":[172],"remain":[174],"(up":[180],"70%":[182],"less":[183,188],"KL":[184],"divergence),":[185],"resulting":[186],"RL-training.":[192],"This":[193],"reduced":[194],"drift":[195],"preserves":[197],"plasticity:":[198],"after":[199],"training":[200],"on":[201,226],"one":[202],"task,":[203],"FST":[204,229],"effectively":[209],"subsequent":[212],"task":[213,223,235],"parameter-only":[215,237],"models.":[217],"continual":[219],"scenarios,":[221],"where":[222],"domains":[224],"change":[225],"fly,":[228],"continues":[230],"acquire":[232],"each":[233],"new":[234],"RL":[238],"stalls.":[239]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-14T00:00:00"}
