{"id":"https://openalex.org/W7157423113","doi":"https://doi.org/10.48550/arxiv.2604.23336","title":"Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA","display_name":"Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA","publication_year":2026,"publication_date":"2026-04-25","ids":{"openalex":"https://openalex.org/W7157423113","doi":"https://doi.org/10.48550/arxiv.2604.23336"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.23336","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23336","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.2604.23336","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134778706","display_name":"Teng Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Teng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134820460","display_name":"Sheng Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Sheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134764127","display_name":"Feixiang Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Feixiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134810355","display_name":"Xiaoyu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xiaoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134798251","display_name":"Qingqing Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Qingqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134815350","display_name":"Hongyan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Hongyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134803012","display_name":"Luo Ji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji, Luo","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.29980000853538513,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.29980000853538513,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.26980000734329224,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.1703999936580658,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/embedding","display_name":"Embedding","score":0.7544000148773193},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4636000096797943},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.4318999946117401},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.4284999966621399},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.39890000224113464},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.3707999885082245},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.3400999903678894},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.3262999951839447}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7544000148773193},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7188000082969666},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4860999882221222},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4636000096797943},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.4318999946117401},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4300999939441681},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.4284999966621399},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.39890000224113464},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3707999885082245},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.3262999951839447},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.305400013923645},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.2924000024795532},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.29100000858306885},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2676999866962433},{"id":"https://openalex.org/C24755975","wikidata":"https://www.wikidata.org/wiki/Q4943354","display_name":"Boolean conjunctive query","level":5,"score":0.251800000667572},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.23336","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23336","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.2604.23336","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.23336","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":[{"score":0.7337352633476257,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Unlike":[0],"traditional":[1,233],"fact-based":[2],"retrieval,":[3],"rationale-based":[4,213],"retrieval":[5,234],"typically":[6],"necessitates":[7],"cross-encoding":[8],"of":[9,77,164,177],"query-document":[10,37],"pairs":[11],"using":[12],"large":[13],"language":[14],"models,":[15],"incurring":[16],"substantial":[17],"computational":[18],"costs.":[19],"To":[20,94,159],"address":[21],"this":[22,96,152],"limitation,":[23],"we":[24,167],"propose":[25],"Rabtriever,":[26],"which":[27,50,117],"independently":[28],"encodes":[29],"queries":[30],"and":[31,58,126,155,201,218,240],"documents,":[32],"while":[33],"providing":[34],"comparable":[35,243],"cross":[36],"comprehension":[38],"capabilities":[39],"to":[40,55,62,87,180,196,245],"rerankers.":[41],"We":[42,70],"start":[43],"from":[44,102,225],"training":[45],"a":[46,119,133],"LLM-based":[47],"generative":[48],"reranker,":[49],"puts":[51],"the":[52,56,60,64,75,85,89,103,129,138,142,148,156,161,174,182,188,193,226,246],"document":[53,139,194],"prior":[54],"query":[57,92,130],"prompts":[59],"LLM":[61,124,178],"generate":[63],"relevance":[65],"score":[66],"by":[67],"log":[68],"probabilities.":[69],"then":[71,115,146],"employ":[72],"it":[73],"as":[74,84,141,237],"teacher":[76,157],"an":[78,170],"on-policy":[79,165],"distillation":[80],"framework,":[81],"with":[82,105,137,221,242],"Rabtriever":[83,98,186,206,228],"student":[86],"reconstruct":[88],"teacher's":[90,189],"contextual-aware":[91],"embedding.":[93,158],"achieve":[95],"effect,":[97],"is":[99,114],"first":[100],"initialized":[101],"teacher,":[104],"parameters":[106],"frozen.":[107],"The":[108],"Joint-Embedding":[109],"Predictive":[110],"Architecture":[111],"(JEPA)":[112],"paradigm":[113],"adopted,":[116],"integrates":[118],"lightweight,":[120],"trainable":[121],"predictor":[122],"between":[123,151],"layers":[125],"heads,":[127],"projecting":[128],"embedding":[131,140,154],"into":[132],"new":[134],"hidden":[135],"space,":[136],"latent":[143],"vector.":[144],"JEPA":[145],"minimizes":[147],"distribution":[149],"difference":[150],"projected":[153],"strengthen":[160],"sampling":[162],"efficiency":[163],"distillation,":[166],"also":[168,229],"add":[169],"auxiliary":[171],"loss":[172],"on":[173,192,232],"reverse":[175],"KL":[176],"logits,":[179],"reshape":[181],"student's":[183],"logit":[184],"distribution.":[185],"optimizes":[187],"quadratic":[190],"complexity":[191],"length":[195],"linear,":[197],"verified":[198],"both":[199],"theoretically":[200],"empirically.":[202],"Experiments":[203],"show":[204],"that":[205],"outperforms":[207],"different":[208],"retriever":[209,248],"baselines":[210],"across":[211],"diverse":[212],"tasks,":[214],"including":[215],"empathetic":[216],"conversations":[217],"robotic":[219],"manipulations,":[220],"minor":[222],"accuracy":[223],"degradation":[224],"reranker.":[227],"generalizes":[230],"well":[231],"benchmarks":[235],"such":[236],"MS":[238],"MARCO":[239],"BEIR,":[241],"performance":[244],"best":[247],"baseline.":[249]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-29T00:00:00"}
