{"id":"https://openalex.org/W7162756341","doi":"https://doi.org/10.48550/arxiv.2605.29584","title":"GAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in Knowledge Base Question Answering","display_name":"GAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in Knowledge Base Question Answering","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162756341","doi":"https://doi.org/10.48550/arxiv.2605.29584"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.29584","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29584","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.2605.29584","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137372390","display_name":"Xin Sun","orcid":"https://orcid.org/0000-0002-7313-4865"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137343703","display_name":"Jianan Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Jianan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102157705","display_name":"Zhongqi Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhongqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137388428","display_name":"Qiang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Qiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137382133","display_name":"Shu Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Shu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137361850","display_name":"Bowen Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Bowen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137353850","display_name":"\u738b\u4f1f\u5f3a Wang Weiqiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Weiqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137398874","display_name":"Zilei Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zilei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137375319","display_name":"Liang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Liang","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":0.5259000062942505,"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":0.5259000062942505,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.18549999594688416,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.0786999985575676,"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/executable","display_name":"Executable","score":0.718500018119812},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.656499981880188},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.5752000212669373},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5594000220298767},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.45649999380111694},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4490000009536743},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4138999879360199}],"concepts":[{"id":"https://openalex.org/C160145156","wikidata":"https://www.wikidata.org/wiki/Q778586","display_name":"Executable","level":2,"score":0.718500018119812},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.656499981880188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5889999866485596},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.5752000212669373},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5594000220298767},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49129998683929443},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.45649999380111694},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4490000009536743},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4138999879360199},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3125},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.29584","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29584","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.2605.29584","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29584","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reinforcement":[0],"learning":[1],"(RL)":[2],"is":[3,49,156],"a":[4,15,86,150],"natural":[5],"fit":[6],"for":[7,52,73,79],"agentic":[8],"knowledge":[9],"base":[10],"question":[11],"answering":[12],"(KBQA),":[13],"where":[14],"model":[16],"must":[17],"issue":[18],"executable":[19,64],"actions,":[20],"observe":[21],"knowledge-base":[22],"feedback,":[23],"and":[24,121,127,180],"eventually":[25],"return":[26],"an":[27],"answer.":[28],"However,":[29],"current":[30,140,172],"RL-based":[31],"KBQA":[32,55],"systems":[33],"mainly":[34,72],"optimize":[35],"sparse":[36],"rewards":[37],"from":[38],"the":[39,114,160,171,175],"final":[40],"answer,":[41],"leaving":[42],"intermediate":[43,115],"action":[44,65,147],"errors":[45],"weakly":[46],"supervised.":[47],"This":[48],"especially":[50],"limiting":[51],"logical-form":[53],"annotated":[54],"benchmarks:":[56],"gold":[57,102,122,132,146],"logical":[58],"forms":[59],"can":[60],"be":[61],"converted":[62],"into":[63],"sequences,":[66],"but":[67],"existing":[68],"pipelines":[69],"use":[70],"them":[71],"warm-start":[74],"data":[75],"construction":[76],"rather":[77],"than":[78],"on-policy":[80,105],"RL":[81],"updates.":[82],"We":[83],"propose":[84],"GAPD,":[85],"training-time":[87],"Gold-Action":[88],"Policy":[89],"Distillation":[90],"framework":[91],"that":[92],"adds":[93],"dense":[94],"token-level":[95],"guidance":[96],"to":[97,131,159],"outcome-based":[98],"RL.":[99],"To":[100],"align":[101],"actions":[103],"with":[104],"student":[106,119,129,162],"rollouts,":[107],"GAPD":[108,168],"uses":[109],"MID-ANCHOR":[110],"MATCHING:":[111],"it":[112],"treats":[113],"entities":[116],"reached":[117],"during":[118],"exploration":[120],"execution":[123],"as":[124,149],"state":[125,173],"anchors,":[126],"matches":[128],"states":[130,133],"through":[134],"these":[135],"explored":[136],"entity":[137],"sets.":[138],"The":[139],"policy":[141,163],"conditioned":[142],"on":[143,177],"this":[144],"aligned":[145],"serves":[148],"stop-gradient":[151],"teacher,":[152],"whose":[153],"token":[154],"distribution":[155],"distilled":[157],"back":[158],"ordinary":[161],"over":[164],"generated":[165],"action-token":[166],"spans.":[167],"consistently":[169],"surpasses":[170],"of":[174],"art":[176],"WebQSP,":[178],"GrailQA,":[179],"GraphQ.":[181]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}