{"id":"https://openalex.org/W7169867614","doi":"https://doi.org/10.48550/arxiv.2607.16246","title":"Let the Data Decide: Supervision Analysis, Capability Trade-offs, and Adaptive Objective Routing in Continued Pre-Training via Off-Policy Distillation","display_name":"Let the Data Decide: Supervision Analysis, Capability Trade-offs, and Adaptive Objective Routing in Continued Pre-Training via Off-Policy Distillation","publication_year":2026,"publication_date":"2026-06-26","ids":{"openalex":"https://openalex.org/W7169867614","doi":"https://doi.org/10.48550/arxiv.2607.16246"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.16246","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.16246","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":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.2607.16246","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139661638","display_name":"Jiangan Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Jiangan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141250572","display_name":"Zhixuan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5141256279","display_name":"Han Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Han","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.3402000069618225,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.3402000069618225,"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/T10028","display_name":"Topic Modeling","score":0.22020000219345093,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.0843999981880188,"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/distillation","display_name":"Distillation","score":0.6093000173568726},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.474700003862381},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.40959998965263367},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.35429999232292175},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.3409000039100647},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.33070001006126404},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.3190000057220459}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6173999905586243},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.6093000173568726},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.474700003862381},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.40959998965263367},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.34139999747276306},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C154030694","wikidata":"https://www.wikidata.org/wiki/Q1436074","display_name":"Fractionating column","level":3,"score":0.3052000105381012},{"id":"https://openalex.org/C105168734","wikidata":"https://www.wikidata.org/wiki/Q1867977","display_name":"Refinery","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C187029079","wikidata":"https://www.wikidata.org/wiki/Q958679","display_name":"Cognitive reframing","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.2624000012874603}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.16246","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.16246","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":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.2607.16246","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.16246","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":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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Off-policy":[0],"distillation":[1,28,129,206],"is":[2],"now":[3],"central":[4],"to":[5,84,147,153,175],"large":[6],"language":[7],"model":[8,17],"pre-training,":[9],"yet":[10],"how":[11,40,55],"training":[12,42],"data,":[13],"objective":[14,43,60,68,73,139,186],"parameterization,":[15],"and":[16,47,50,70,80,92,110,114,149,151],"capabilities":[18],"interact":[19],"remains":[20],"poorly":[21],"characterized.":[22],"We":[23,62,135],"studies":[24],"top-$k$-truncated,":[25],"temperature-scaled":[26],"off-policy":[27,205],"by":[29],"decomposing":[30],"this":[31,82,98],"problem":[32,212],"into":[33],"two":[34],"questions:":[35],"an":[36],"\\emph{objective-to-capability}":[37],"analysis":[38,53],"of":[39,54,197],"the":[41,66,71,120,178,195,198],"shapes":[44],"token-level":[45,164],"supervision":[46],"downstream":[48],"performance,":[49],"a":[51,85,125,141,208,215],"\\emph{data-to-objective}":[52],"data":[56,155],"heterogeneity":[57],"should":[58],"inform":[59],"routing.":[61],"first":[63],"show":[64,115],"that":[65,119,144,184],"language-modeling":[67],"($L_{\\mathrm{LM}}$)":[69],"knowledge-distillation":[72],"($L_{\\mathrm{KD}}$)":[74],"induce":[75],"systematically":[76],"different":[77],"capability":[78],"profiles,":[79],"trace":[81],"divergence":[83],"gradient-level":[86],"tension":[87],"between":[88],"\\emph{direct":[89],"observed-token":[90,107,168],"reinforcement}":[91],"\\emph{teacher-supported":[93],"alternative":[94],"supervision}.":[95],"To":[96],"quantify":[97],"tension,":[99],"we":[100],"introduce":[101],"diagnostic":[102],"metrics":[103],"--":[104,113],"support":[105,121],"coverage,":[106],"probability":[108,133,169],"mass,":[109],"teacher-distribution":[111],"concentration":[112],"via":[116,204],"controlled":[117],"sweeps":[118],"size":[122],"$k$":[123],"governs":[124],"coverage-sharpness":[126],"trade-off,":[127],"while":[128],"temperature":[130],"controls":[131],"within-support":[132],"allocation.":[134],"then":[136],"examine":[137],"adaptive":[138],"routing:":[140],"domain-level":[142],"policy":[143],"applies":[145],"$L_{\\mathrm{LM}}$":[146],"math":[148],"code":[150],"$L_{\\mathrm{KD}}$":[152],"general-domain":[154],"yields":[156],"consistent":[157],"gains":[158],"over":[159],"both":[160],"single-objective":[161,179],"baselines,":[162],"whereas":[163],"routing":[165,187,191,199],"based":[166],"on":[167,190,194],"mass":[170],"or":[171],"teacher":[172],"entropy":[173],"fails":[174],"consistently":[176],"match":[177],"baseline.":[180],"These":[181],"results":[182],"suggest":[183],"effective":[185],"depends":[188],"less":[189],"granularity":[192],"than":[193,214],"quality":[196],"signal,":[200],"reframing":[201],"continued":[202],"pre-training":[203],"as":[207],"structured,":[209],"data-conditional":[210],"supervision-design":[211],"rather":[213],"global":[216],"hyperparameter":[217],"choice.":[218]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-22T00:00:00"}
