{"id":"https://openalex.org/W7133220763","doi":"https://doi.org/10.48550/arxiv.2602.23881","title":"LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding","display_name":"LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding","publication_year":2026,"publication_date":"2026-02-27","ids":{"openalex":"https://openalex.org/W7133220763","doi":"https://doi.org/10.48550/arxiv.2602.23881"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.23881","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127830269","display_name":"Alexander Samarin","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Samarin, Alexander","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098821251","display_name":"Sergei Krutikov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krutikov, Sergei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127807012","display_name":"Anton Shevtsov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shevtsov, Anton","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127846761","display_name":"Sergei Skvortsov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Skvortsov, Sergei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119932368","display_name":"Filipp Fisin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fisin, Filipp","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5110806776","display_name":"Alexander Golubev","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Golubev, Alexander","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5127830269"],"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":0.15479999780654907,"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":0.15479999780654907,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.1468999981880188,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.09290000051259995,"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/speedup","display_name":"Speedup","score":0.5455999970436096},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.5024999976158142},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.486299991607666},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.4374000132083893},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.4253000020980835},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.3953000009059906},{"id":"https://openalex.org/keywords/acceptance-testing","display_name":"Acceptance testing","score":0.39250001311302185}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6621999740600586},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5455999970436096},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5024999976158142},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.486299991607666},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.4374000132083893},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.4253000020980835},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.3953000009059906},{"id":"https://openalex.org/C131377759","wikidata":"https://www.wikidata.org/wiki/Q322514","display_name":"Acceptance testing","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.3801000118255615},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3693000078201294},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.36169999837875366},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.35249999165534973},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32850000262260437},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.29190000891685486},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.2678000032901764},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25859999656677246},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.2556999921798706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2540999948978424}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.23881","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.23881","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.23881","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":"pmh:doi:10.48550/arxiv.2602.23881","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4615367650985718,"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":{"Speculative":[0],"decoding":[1],"accelerates":[2],"autoregressive":[3],"large":[4],"language":[5],"model":[6,14],"(LLM)":[7],"inference":[8],"by":[9,25,34],"using":[10],"a":[11,46,171],"lightweight":[12],"draft":[13,61,100,177],"to":[15,68,109,122,142,152,174],"propose":[16,85],"candidate":[17],"tokens":[18],"that":[19,91],"are":[20,150],"then":[21],"verified":[22],"in":[23,115,144],"parallel":[24],"the":[26,35,56,123,175],"target":[27,93,104],"model.":[28],"The":[29],"speedup":[30],"is":[31],"significantly":[32],"determined":[33],"acceptance":[36,53,78,94,116,146],"rate,":[37],"yet":[38],"standard":[39,124],"training":[40,89,167,178],"minimizes":[41],"Kullback-Leibler":[42],"(KL)":[43],"divergence":[44,51],"as":[45],"proxy":[47],"objective.":[48],"While":[49],"KL":[50,73],"and":[52,102,134,137,158],"rate":[54],"share":[55],"same":[57],"global":[58],"optimum,":[59],"small":[60],"models,":[62,105],"having":[63],"limited":[64],"capacity,":[65],"typically":[66],"converge":[67],"suboptimal":[69],"solutions":[70],"where":[71],"minimizing":[72],"does":[74],"not":[75],"guarantee":[76],"maximizing":[77],"rate.":[79,95],"To":[80],"address":[81],"this":[82],"issue,":[83],"we":[84],"LK":[86,148],"losses,":[87],"special":[88],"objectives":[90],"directly":[92,161],"Comprehensive":[96],"experiments":[97],"across":[98,118],"four":[99],"architectures":[101],"six":[103],"ranging":[106],"from":[107],"8B":[108],"685B":[110],"parameters,":[111],"demonstrate":[112],"consistent":[113],"improvements":[114],"metrics":[117],"all":[119],"configurations":[120],"compared":[121],"KL-based":[125],"training.":[126],"We":[127],"evaluate":[128],"our":[129],"approach":[130],"on":[131],"general,":[132],"coding":[133],"math":[135],"domains":[136],"report":[138],"gains":[139],"of":[140],"up":[141],"8-10%":[143],"average":[145],"length.":[147],"losses":[149],"easy":[151],"implement,":[153],"introduce":[154],"no":[155],"computational":[156],"overhead":[157],"can":[159],"be":[160],"integrated":[162],"into":[163],"any":[164],"existing":[165,176],"speculator":[166],"framework,":[168],"making":[169],"them":[170],"compelling":[172],"alternative":[173],"objectives.":[179]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-03-03T00:00:00"}