{"id":"https://openalex.org/W7119472079","doi":"https://doi.org/10.48550/arxiv.2601.03858","title":"What Does Loss Optimization Actually Teach, If Anything? Knowledge Dynamics in Continual Pre-training of LLMs","display_name":"What Does Loss Optimization Actually Teach, If Anything? Knowledge Dynamics in Continual Pre-training of LLMs","publication_year":2026,"publication_date":"2026-01-07","ids":{"openalex":"https://openalex.org/W7119472079","doi":"https://doi.org/10.48550/arxiv.2601.03858"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.03858","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.03858","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.2601.03858","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047699997","display_name":"Seyed Mahed Mousavi","orcid":"https://orcid.org/0000-0002-7790-7730"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mousavi, Seyed Mahed","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093660464","display_name":"Simone Alghisi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alghisi, Simone","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5062879885","display_name":"Giuseppe Riccardi","orcid":"https://orcid.org/0000-0002-0739-8184"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Riccardi, Giuseppe","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5047699997"],"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/T10918","display_name":"Memory Processes and Influences","score":0.36329999566078186,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10918","display_name":"Memory Processes and Influences","score":0.36329999566078186,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.12309999763965607,"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/T10028","display_name":"Topic Modeling","score":0.0794999971985817,"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/benchmark","display_name":"Benchmark (surveying)","score":0.5289000272750854},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4717000126838684},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.4571000039577484},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4521999955177307},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.4302000105381012},{"id":"https://openalex.org/keywords/knowledge-acquisition","display_name":"Knowledge acquisition","score":0.3727000057697296}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5289000272750854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5023999810218811},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4717000126838684},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.4571000039577484},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4521999955177307},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C2777220311","wikidata":"https://www.wikidata.org/wiki/Q6423340","display_name":"Knowledge acquisition","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35830000042915344},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3483000099658966},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.3393000066280365},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3382999897003174},{"id":"https://openalex.org/C18619997","wikidata":"https://www.wikidata.org/wiki/Q47627","display_name":"Experimental economics","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C79416737","wikidata":"https://www.wikidata.org/wiki/Q2305519","display_name":"Social learning","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2624000012874603}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.03858","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.03858","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.2601.03858","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.03858","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":[{"score":0.7868353128433228,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Continual":[0],"Pre-Training":[1],"(CPT)":[2],"is":[3,112,122,161],"widely":[4],"used":[5],"for":[6,21,148],"acquiring":[7],"and":[8,57,73,96,101,114,128,152,168],"updating":[9],"factual":[10,55,110],"knowledge":[11,22,39,70,88,141],"in":[12,75,166],"LLMs.":[13],"This":[14],"practice":[15],"treats":[16],"loss":[17,106,159],"as":[18,37,105],"a":[19,38,44,50],"proxy":[20],"learning,":[23],"while":[24,109],"offering":[25],"no":[26],"grounding":[27],"into":[28,62],"how":[29,85],"it":[30],"changes":[31,74],"during":[32,90],"training.":[33,91],"We":[34,48,82],"study":[35],"CPT":[36,64,86,98,167],"learning":[40,102,111,121,164,177],"process":[41],"rather":[42],"than":[43],"solely":[45],"optimization":[46,100,160],"problem.":[47],"construct":[49],"controlled,":[51],"distribution-matched":[52],"benchmark":[53],"of":[54,69,140,171],"documents":[56],"interleave":[58],"diagnostic":[59],"probes":[60],"directly":[61],"the":[63],"loop,":[65],"enabling":[66],"epoch-level":[67],"measurement":[68],"acquisition":[71,150],"dynamics":[72],"Out-Of-Domain":[76],"(OOD)":[77],"general":[78],"skills":[79],"(e.g.,":[80],"math).":[81],"further":[83],"analyze":[84],"reshapes":[87],"circuits":[89],"Across":[92],"three":[93],"instruction-tuned":[94],"LLMs":[95],"multiple":[97],"strategies,":[99],"systematically":[103],"diverge":[104],"decreases":[107],"monotonically":[108],"unstable":[113],"non-monotonic.":[115],"Acquired":[116],"facts":[117],"are":[118],"rarely":[119],"consolidated,":[120],"strongly":[123],"conditioned":[124],"on":[125,175],"prior":[126],"exposure,":[127],"OOD":[129],"performance":[130],"degrades":[131],"from":[132],"early":[133],"epochs.":[134],"Circuit":[135],"analysis":[136],"reveals":[137],"rapid":[138],"reconfiguration":[139],"pathways":[142],"across":[143],"epochs,":[144],"providing":[145],"an":[146],"explanation":[147],"narrow":[149],"windows":[151],"systematic":[153],"forgetting.":[154],"These":[155],"results":[156],"show":[157],"that":[158],"misaligned":[162],"with":[163],"progress":[165],"motivate":[169],"evaluation":[170],"stopping":[172],"criteria":[173],"based":[174],"task-level":[176],"dynamics.":[178]},"counts_by_year":[],"updated_date":"2026-01-09T23:14:04.187858","created_date":"2026-01-09T00:00:00"}