{"id":"https://openalex.org/W7162125081","doi":"https://doi.org/10.48550/arxiv.2605.22675","title":"Self-Policy Distillation via Capability-Selective Subspace Projection","display_name":"Self-Policy Distillation via Capability-Selective Subspace Projection","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7162125081","doi":"https://doi.org/10.48550/arxiv.2605.22675"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.22675","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22675","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":null,"license_id":null,"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.2605.22675","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077839397","display_name":"\u597d\u5149 \u96c5","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao, Guangya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136733104","display_name":"Yitong Shang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shang, Yitong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136732930","display_name":"Yunbo Long","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Long, Yunbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136744073","display_name":"Zhuokai Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Zhuokai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136777640","display_name":"Hanxue Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Hanxue","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/T10028","display_name":"Topic Modeling","score":0.32409998774528503,"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":0.32409998774528503,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.2558000087738037,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.07739999890327454,"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/subspace-topology","display_name":"Subspace topology","score":0.6852999925613403},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6837999820709229},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.6470000147819519},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5412999987602234},{"id":"https://openalex.org/keywords/disk-formatting","display_name":"Disk formatting","score":0.48399999737739563},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.4458000063896179},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.40049999952316284},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.38449999690055847}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7506999969482422},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6852999925613403},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6837999820709229},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.6470000147819519},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5412999987602234},{"id":"https://openalex.org/C88006597","wikidata":"https://www.wikidata.org/wiki/Q690117","display_name":"Disk formatting","level":2,"score":0.48399999737739563},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.4458000063896179},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42739999294281006},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.40049999952316284},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.38449999690055847},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3617999851703644},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3310000002384186},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3142000138759613},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.3034000098705292},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2773999869823456},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.27570000290870667},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.22675","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22675","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.22675","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.22675","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":null,"license_id":null,"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":{"Self-distillation":[0],"bootstraps":[1],"large":[2],"language":[3],"models":[4],"(LLMs)":[5],"by":[6],"training":[7],"on":[8,17,48,125,139],"their":[9],"own":[10,123],"generations.":[11],"However,":[12],"existing":[13],"methods":[14,172],"either":[15],"rely":[16],"external":[18,111,174],"signals":[19,175],"to":[20,60,94,166,178],"curate":[21],"self-generated":[22,69],"outputs":[23,70,143],"(e.g.,":[24],"correctness":[25],"filtering,":[26],"execution":[27],"feedback,":[28],"and":[29,35,46,58,82,137,157,176],"reward":[30],"search),":[31],"which":[32,104],"are":[33],"costly":[34],"unavailable":[36],"for":[37,88],"the":[38,86,89,121,140],"best-performing":[39],"frontier":[40],"models,":[41],"or":[42],"skip":[43],"curation":[44],"entirely":[45],"train":[47],"all":[49],"raw":[50,142],"outputs,":[51],"an":[52],"approach":[53],"that":[54,68,162],"is":[55],"often":[56],"domain-specific":[57],"hard":[59],"generalize.":[61],"Both":[62],"also":[63],"share":[64],"a":[65,116],"deeper":[66],"weakness":[67],"entangle":[71],"task-relevant":[72],"capability":[73,91,107,118],"with":[74,144],"others,":[75],"such":[76],"as":[77],"stylistic":[78],"patterns,":[79],"formatting":[80],"artifacts,":[81],"model-specific":[83],"errors,":[84],"diluting":[85],"signal":[87],"specific":[90],"one":[92],"aims":[93],"improve.":[95],"In":[96],"this":[97,133],"paper,":[98],"we":[99,160],"propose":[100],"Self-Policy":[101],"Distillation":[102],"(SPD),":[103],"achieves":[105,164],"generalizable,":[106],"selective":[108],"without":[109,173],"any":[110],"signal.":[112],"Specifically,":[113],"SPD":[114,163,185],"extracts":[115],"low-rank":[117],"subspace":[119,134],"from":[120],"model's":[122],"gradients":[124],"correctness-defining":[126],"tokens,":[127],"projects":[128],"key-value":[129],"(KV)":[130],"activations":[131],"into":[132],"during":[135],"self-generation,":[136],"fine-tunes":[138],"resulting":[141],"standard":[145],"next-token":[146],"prediction":[147],"loss.":[148],"Through":[149],"extensive":[150],"experiments":[151],"across":[152],"code":[153],"generation,":[154],"mathematical":[155],"reasoning,":[156],"multiple-choice":[158],"QA,":[159],"show":[161],"up":[165,177],"13%":[167],"improvement":[168,180],"over":[169,181],"state-of-the-art":[170],"self-distillation":[171],"16%":[179],"pre-trained":[182],"baselines.":[183],"Notably,":[184],"demonstrates":[186],"superior":[187],"generalizability,":[188],"achieving":[189],"15%":[190],"better":[191],"performance":[192],"under":[193],"out-of-domain":[194],"generalization":[195],"settings.":[196]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-23T00:00:00"}
