{"id":"https://openalex.org/W7162574072","doi":"https://doi.org/10.48550/arxiv.2605.26844","title":"Not All Disagreement Is Learnable: Token Teachability in On-Policy Distillation","display_name":"Not All Disagreement Is Learnable: Token Teachability in On-Policy Distillation","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162574072","doi":"https://doi.org/10.48550/arxiv.2605.26844"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.26844","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26844","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.26844","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137149426","display_name":"Yuanyi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yuanyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135961740","display_name":"Su Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Su","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137180326","display_name":"Yanggan Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Yanggan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043684987","display_name":"Pengkai Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Pengkai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137088985","display_name":"Yifan Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137097827","display_name":"Zhaoyi Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Zhaoyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137093436","display_name":"Congkai Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Congkai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137171836","display_name":"Jianmin Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Jianmin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137164090","display_name":"Hongxia Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Hongxia","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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.21860000491142273,"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.21860000491142273,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.11389999836683273,"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":0.065700002014637,"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/exploit","display_name":"Exploit","score":0.6773999929428101},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6384999752044678},{"id":"https://openalex.org/keywords/cognitive-reframing","display_name":"Cognitive reframing","score":0.6152999997138977},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.613099992275238},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.5333999991416931},{"id":"https://openalex.org/keywords/imperfect","display_name":"Imperfect","score":0.3743000030517578}],"concepts":[{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6773999929428101},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6384999752044678},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6223999857902527},{"id":"https://openalex.org/C187029079","wikidata":"https://www.wikidata.org/wiki/Q958679","display_name":"Cognitive reframing","level":2,"score":0.6152999997138977},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.613099992275238},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.5333999991416931},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.3743000030517578},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.35199999809265137},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3287999927997589},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31949999928474426},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30570000410079956},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C2778648169","wikidata":"https://www.wikidata.org/wiki/Q967768","display_name":"Compatibility (geochemistry)","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2606000006198883}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.26844","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26844","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.26844","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26844","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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.7508609294891357}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"On-policy":[0],"distillation":[1],"(OPD)":[2],"trains":[3],"a":[4,44,60,124],"student":[5],"on":[6],"its":[7],"own":[8],"rollouts":[9],"with":[10,81,153],"token-level":[11,37],"teacher":[12,38,72,86,173],"supervision.":[13],"Recent":[14],"selective":[15,168],"OPD":[16,22,122,131,152,169],"methods":[17],"exploit":[18],"the":[19,71,77,85,91],"non-uniformity":[20],"of":[21],"signals":[23,39,174],"by":[24,116],"prioritizing":[25],"high-entropy":[26],"or":[27,139],"high-disagreement":[28],"tokens.":[29,179],"We":[30,95],"revisit":[31],"this":[32,97,117],"principle":[33],"and":[34,103,143,158,162],"ask:":[35],"which":[36],"are":[40],"actually":[41],"learnable?":[42],"Using":[43],"fixed-context":[45,109],"diagnostic":[46],"that":[47,55,105,129],"measures":[48],"same-context":[49],"teacher-student":[50,146],"KL":[51,57,113],"reduction,":[52],"we":[53,119],"show":[54,104],"raw":[56,112],"disagreement":[58],"is":[59],"coarse":[61],"proxy":[62],"for":[63],"learning":[64],"value.":[65],"It":[66],"conflates":[67],"learnable":[68,172],"disagreement,":[69,83],"where":[70,84],"assigns":[73],"corrective":[74],"mass":[75,88],"to":[76,133],"student's":[78,92],"top-K":[79],"candidates,":[80],"incompatible":[82],"places":[87],"mostly":[89],"off":[90],"current":[93],"support.":[94],"formalize":[96],"local":[98],"compatibility":[99],"as":[100,170],"token":[101],"teachability":[102],"it":[106],"better":[107],"predicts":[108],"improvement":[110],"than":[111,176],"alone.":[114],"Motivated":[115],"finding,":[118],"propose":[120],"Teachability-Aware":[121],"(TA-OPD),":[123],"lightweight":[125],"token-position":[126],"selection":[127],"method":[128],"applies":[130],"loss":[132],"high-teachability":[134],"positions":[135],"without":[136],"reward":[137],"models":[138],"verifiers.":[140],"Across":[141],"Qwen2.5":[142],"Qwen":[144],"3":[145],"settings,":[147],"TA-OPD":[148],"often":[149],"surpasses":[150],"full-token":[151],"only":[154],"5%":[155],"retained":[156],"tokens":[157],"improves":[159],"over":[160],"entropy-":[161],"divergence-based":[163],"baselines.":[164],"Our":[165],"results":[166],"reframe":[167],"selecting":[171],"rather":[175],"merely":[177],"salient":[178]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-28T00:00:00"}
