{"id":"https://openalex.org/W7133526495","doi":"https://doi.org/10.48550/arxiv.2603.02730","title":"APAO: Bridging the Training-Inference Gap in Generative Recommendation via Adaptive Prefix-Aware Optimization","display_name":"APAO: Bridging the Training-Inference Gap in Generative Recommendation via Adaptive Prefix-Aware Optimization","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7133526495","doi":"https://doi.org/10.48550/arxiv.2603.02730"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.02730","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02730","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.2603.02730","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128121455","display_name":"Yuanqing Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yuanqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128040467","display_name":"Yifan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040222654","display_name":"Weizhi Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Weizhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128097461","display_name":"Zhiqiang Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Zhiqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128123321","display_name":"Min Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Min","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/T10203","display_name":"Recommender Systems and Techniques","score":0.9279999732971191,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9279999732971191,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.007400000002235174,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.0044999998062849045,"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/inference","display_name":"Inference","score":0.741100013256073},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5942000150680542},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.579800009727478},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5149999856948853},{"id":"https://openalex.org/keywords/prefix","display_name":"Prefix","score":0.40380001068115234},{"id":"https://openalex.org/keywords/beam-search","display_name":"Beam search","score":0.3774999976158142},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.32339999079704285}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7519000172615051},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.741100013256073},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6116999983787537},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5942000150680542},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.579800009727478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5475999712944031},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5149999856948853},{"id":"https://openalex.org/C141603448","wikidata":"https://www.wikidata.org/wiki/Q134830","display_name":"Prefix","level":2,"score":0.40380001068115234},{"id":"https://openalex.org/C19889080","wikidata":"https://www.wikidata.org/wiki/Q2835852","display_name":"Beam search","level":3,"score":0.3774999976158142},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C10485038","wikidata":"https://www.wikidata.org/wiki/Q48996162","display_name":"Hyperparameter optimization","level":3,"score":0.3221000134944916},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.28519999980926514},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2770000100135803},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2524999976158142},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.02730","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02730","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.2603.02730","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02730","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":{"Generative":[0],"recommendation":[1,34,181],"has":[2],"recently":[3],"emerged":[4],"as":[5,16,44],"a":[6,61],"promising":[7],"paradigm":[8],"for":[9],"sequential":[10],"recommendation.":[11],"It":[12],"formulates":[13],"the":[14,24,82,101,115,119,134,142,159,173],"task":[15],"an":[17,125],"autoregressive":[18],"generation":[19],"process,":[20],"predicting":[21],"tokens":[22,69],"of":[23,163],"next":[25],"item":[26,84],"conditioned":[27],"on":[28,133],"user":[29],"interaction":[30],"histories.":[31],"Existing":[32],"generative":[33,180],"models":[35],"are":[36,70],"typically":[37],"trained":[38],"with":[39,118],"token-level":[40],"likelihood":[41],"objectives":[42],"such":[43],"cross-entropy":[45],"loss,":[46],"while":[47,73],"employing":[48],"beam":[49,74,150],"search":[50,75,151],"during":[51,79,138],"inference":[52,120],"to":[53,60,85,112,145,157],"generate":[54],"ranked":[55],"candidates.":[56],"However,":[57],"this":[58,97],"leads":[59],"fundamental":[62],"training-inference":[63,174],"inconsistency:":[64],"standard":[65],"training":[66,116],"assumes":[67],"ground-truth":[68],"always":[71],"available,":[72],"prunes":[76],"low-probability":[77],"branches":[78],"inference,":[80],"causing":[81],"correct":[83,147],"be":[86],"prematurely":[87],"discarded":[88],"when":[89],"its":[90],"prefixes":[91,137],"receive":[92],"low":[93],"scores.":[94],"To":[95],"address":[96],"issue,":[98],"we":[99,123],"propose":[100],"Adaptive":[102],"Prefix-Aware":[103],"Optimization":[104],"(APAO)":[105],"framework,":[106],"which":[107],"introduces":[108],"prefix-level":[109],"optimization":[110,128],"losses":[111],"better":[113],"align":[114],"objective":[117],"setting.":[121],"Furthermore,":[122],"design":[124],"adaptive":[126],"worst-prefix":[127],"strategy":[129],"that":[130,169],"dynamically":[131],"focuses":[132],"most":[135],"vulnerable":[136],"training,":[139],"thereby":[140],"enhancing":[141],"model's":[143],"ability":[144],"retain":[146],"candidates":[148],"under":[149],"constraints.":[152],"We":[153],"provide":[154],"theoretical":[155],"analyses":[156],"demonstrate":[158],"effectiveness":[160],"and":[161,176],"efficiency":[162],"our":[164],"framework.":[165],"Extensive":[166],"experiments":[167],"show":[168],"APAO":[170],"consistently":[171],"alleviates":[172],"inconsistency":[175],"improves":[177],"performance":[178],"across":[179],"backbones.":[182],"The":[183],"source":[184],"code":[185],"is":[186],"publicly":[187],"available":[188],"at":[189],"https://github.com/yuyq18/APAO.":[190]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-05T00:00:00"}
