{"id":"https://openalex.org/W7159019538","doi":"https://doi.org/10.48550/arxiv.2604.26951","title":"Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models","display_name":"Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models","publication_year":2026,"publication_date":"2026-04-29","ids":{"openalex":"https://openalex.org/W7159019538","doi":"https://doi.org/10.48550/arxiv.2604.26951"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.26951","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26951","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.2604.26951","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134913711","display_name":"Gongbo Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhang, Gongbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134881643","display_name":"Wen Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Wen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134901798","display_name":"Ye Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Ye","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134926169","display_name":"Li Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5134913711"],"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":0.24130000174045563,"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":0.24130000174045563,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.09160000085830688,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.09059999883174896,"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/distillation","display_name":"Distillation","score":0.763700008392334},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6553999781608582},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6071000099182129},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5667999982833862},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.5546000003814697},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5206000208854675},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.5023999810218811},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.49810001254081726},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4731000065803528}],"concepts":[{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.763700008392334},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6625000238418579},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6553999781608582},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6071000099182129},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5667999982833862},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.5546000003814697},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5206000208854675},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.5023999810218811},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.49810001254081726},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4731000065803528},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4717000126838684},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.4645000100135803},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.45410001277923584},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3853999972343445},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.37560001015663147},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2953000068664551},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.2816999852657318},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27730000019073486},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.26951","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26951","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.2604.26951","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26951","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":0.6572468280792236,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diffusion":[0],"large":[1],"language":[2],"models":[3],"(dLLMs)":[4],"offer":[5],"parallel":[6],"decoding":[7],"and":[8,43,50,76,103,118,125],"bidirectional":[9],"context,":[10],"but":[11],"state-of-the-art":[12],"dLLMs":[13,26],"require":[14],"billions":[15],"of":[16,143],"parameters":[17],"for":[18,25,58,81,163],"competitive":[19],"performance.":[20],"While":[21],"existing":[22],"distillation":[23,71],"methods":[24],"reduce":[27],"inference":[28],"steps":[29],"within":[30],"a":[31,107,130],"single":[32],"architecture,":[33,47],"none":[34],"address":[35],"cross-architecture":[36,59],"knowledge":[37],"transfer,":[38],"in":[39,46,152],"which":[40,68,88],"the":[41,55,82,90,138,164],"teacher":[42],"student":[44,132],"differ":[45],"attention":[48],"mechanism,":[49],"tokenizer.":[51],"We":[52],"present":[53],"TIDE,":[54],"first":[56],"framework":[57],"dLLM":[60],"distillation,":[61],"comprising":[62],"three":[63],"modular":[64],"components:":[65],"(1)":[66],"TIDAL,":[67],"jointly":[69],"modulates":[70],"strength":[72],"across":[73,146],"training":[74],"progress":[75],"diffusion":[77],"timestep":[78],"to":[79,97,161],"account":[80],"teacher's":[83,91],"noise-dependent":[84],"reliability;":[85],"(2)":[86],"CompDemo,":[87],"enriches":[89],"context":[92],"via":[93,133],"complementary":[94],"mask":[95],"splitting":[96],"improve":[98],"predictions":[99],"under":[100],"heavy":[101],"masking;":[102],"(3)":[104],"Reverse":[105],"CALM,":[106],"cross-tokenizer":[108],"objective":[109],"that":[110],"inverts":[111],"chunk-level":[112],"likelihood":[113],"matching,":[114],"yielding":[115,149],"bounded":[116],"gradients":[117],"dual-end":[119],"noise":[120],"filtering.":[121],"Distilling":[122],"8B":[123],"dense":[124],"16B":[126],"MoE":[127],"teachers":[128],"into":[129],"0.6B":[131],"two":[134],"heterogeneous":[135],"pipelines":[136],"outperforms":[137],"baseline":[139],"by":[140],"an":[141],"average":[142],"1.53":[144],"points":[145],"eight":[147],"benchmarks,":[148],"notable":[150],"gains":[151],"code":[153],"generation,":[154],"where":[155],"HumanEval":[156],"scores":[157],"reach":[158],"48.78":[159],"compared":[160],"32.3":[162],"AR":[165],"baseline.":[166]},"counts_by_year":[],"updated_date":"2026-05-01T06:10:29.291645","created_date":"2026-05-01T00:00:00"}
