{"id":"https://openalex.org/W7133353704","doi":"https://doi.org/10.48550/arxiv.2603.01481","title":"Harmonizing Dense and Sparse Signals in Multi-turn RL: Dual-Horizon Credit Assignment for Industrial Sales Agents","display_name":"Harmonizing Dense and Sparse Signals in Multi-turn RL: Dual-Horizon Credit Assignment for Industrial Sales Agents","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133353704","doi":"https://doi.org/10.48550/arxiv.2603.01481"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01481","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01481","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.2603.01481","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127934890","display_name":"Haojin Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Haojin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128030938","display_name":"Ai Jian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jian, Ai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101767039","display_name":"Xinyue Huang","orcid":"https://orcid.org/0000-0002-2053-8583"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Xinyue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127984019","display_name":"Yiwei Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127969940","display_name":"Weipeng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Weipeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127918835","display_name":"Ke Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Ke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127986940","display_name":"Xunliang Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Xunliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5076546255","display_name":"Jingqing Ruan","orcid":"https://orcid.org/0000-0002-4857-9053"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruan, Jingqing","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":0.311599999666214,"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":0.311599999666214,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.12680000066757202,"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":0.10570000112056732,"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/normalization","display_name":"Normalization (sociology)","score":0.6146000027656555},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.5309000015258789},{"id":"https://openalex.org/keywords/fluency","display_name":"Fluency","score":0.5044000148773193},{"id":"https://openalex.org/keywords/dual-language","display_name":"Dual language","score":0.5016999840736389},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4945000112056732},{"id":"https://openalex.org/keywords/repetition","display_name":"Repetition (rhetorical device)","score":0.39969998598098755}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6439999938011169},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6146000027656555},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.5309000015258789},{"id":"https://openalex.org/C2777413886","wikidata":"https://www.wikidata.org/wiki/Q3276013","display_name":"Fluency","level":2,"score":0.5044000148773193},{"id":"https://openalex.org/C2992540719","wikidata":"https://www.wikidata.org/wiki/Q5310213","display_name":"Dual language","level":2,"score":0.5016999840736389},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4945000112056732},{"id":"https://openalex.org/C2776141515","wikidata":"https://www.wikidata.org/wiki/Q1274479","display_name":"Repetition (rhetorical device)","level":2,"score":0.39969998598098755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32249999046325684},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3222000002861023},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.25870001316070557},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01481","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01481","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.2603.01481","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01481","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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4298643171787262}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Optimizing":[0],"large":[1],"language":[2,155],"models":[3],"for":[4,139],"industrial":[5,141],"sales":[6,142],"requires":[7],"balancing":[8],"long-term":[9,95],"commercial":[10],"objectives":[11,96],"(e.g.,":[12],"conversion":[13,121],"rate)":[14],"with":[15,103],"immediate":[16,93],"linguistic":[17],"constraints":[18],"such":[19],"as":[20],"fluency":[21],"and":[22,82,94,128,153],"compliance.":[23],"Conventional":[24],"reinforcement":[25],"learning":[26],"often":[27],"merges":[28],"these":[29],"heterogeneous":[30],"goals":[31],"into":[32],"a":[33,63,104,116,136],"single":[34],"reward,":[35],"causing":[36],"high-magnitude":[37],"session-level":[38,83],"rewards":[39,84],"to":[40,47,97],"overwhelm":[41],"subtler":[42],"turn-level":[43,81],"signals,":[44],"which":[45],"leads":[46],"unstable":[48],"training":[49],"or":[50],"reward":[51],"hacking.":[52],"To":[53],"address":[54],"this":[55],"issue,":[56],"we":[57],"propose":[58],"Dual-Horizon":[59],"Credit":[60],"Assignment":[61],"(DuCA),":[62],"framework":[64],"that":[65,144],"disentangles":[66],"optimization":[67],"across":[68],"time":[69],"scales.":[70],"Its":[71],"core,":[72],"Horizon-Independent":[73],"Advantage":[74],"Normalization":[75],"(HIAN),":[76],"separately":[77],"normalizes":[78],"advantages":[79],"from":[80,91],"before":[85],"fusion,":[86],"ensuring":[87],"balanced":[88],"gradient":[89],"contributions":[90],"both":[92],"the":[98,111,147],"policy":[99],"update.":[100],"Extensive":[101],"experiments":[102],"high-fidelity":[105],"user":[106],"simulator":[107],"show":[108],"DuCA":[109],"outperforms":[110],"state-of-the-art":[112],"GRPO":[113],"baseline,":[114],"achieving":[115],"6.82%":[117],"relative":[118],"improvement":[119,138],"in":[120],"rate,":[122],"reducing":[123],"inter-sentence":[124],"repetition":[125],"by":[126,133],"82.28%,":[127],"lowering":[129],"identity":[130],"detection":[131],"rate":[132],"27.35%,":[134],"indicating":[135],"substantial":[137],"an":[140],"scenario":[143],"effectively":[145],"balances":[146],"dual":[148],"demands":[149],"of":[150],"strategic":[151],"performance":[152],"naturalistic":[154],"generation.":[156]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-04T00:00:00"}