{"id":"https://openalex.org/W7161715558","doi":"https://doi.org/10.48550/arxiv.2605.17648","title":"SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation","display_name":"SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation","publication_year":2026,"publication_date":"2026-05-17","ids":{"openalex":"https://openalex.org/W7161715558","doi":"https://doi.org/10.48550/arxiv.2605.17648"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.17648","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17648","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.2605.17648","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136489486","display_name":"Zaiyi Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Zaiyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136488318","display_name":"Guanghui Min","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Min, Guanghui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102911934","display_name":"Yaochen Zhu","orcid":"https://orcid.org/0000-0001-6266-2788"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yaochen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136477449","display_name":"Liang Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136454722","display_name":"Liangjie Hong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hong, Liangjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136489942","display_name":"Chen Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136467750","display_name":"Jundong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jundong","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/T10203","display_name":"Recommender Systems and Techniques","score":0.9182999730110168,"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"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9182999730110168,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.009800000116229057,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.007499999832361937,"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/security-token","display_name":"Security token","score":0.5893999934196472},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.531499981880188},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.526199996471405},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5227000117301941},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5130000114440918},{"id":"https://openalex.org/keywords/verifiable-secret-sharing","display_name":"Verifiable secret sharing","score":0.40310001373291016}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7458999752998352},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5893999934196472},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.531499981880188},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.526199996471405},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5227000117301941},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5157999992370605},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5130000114440918},{"id":"https://openalex.org/C85847156","wikidata":"https://www.wikidata.org/wiki/Q59015987","display_name":"Verifiable secret sharing","level":3,"score":0.40310001373291016},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.36809998750686646},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36570000648498535},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.35690000653266907},{"id":"https://openalex.org/C154504017","wikidata":"https://www.wikidata.org/wiki/Q853614","display_name":"Identifier","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.17648","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17648","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.2605.17648","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17648","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generative":[0],"recommendation":[1,172,184],"treats":[2],"next-item":[3],"prediction":[4,89],"as":[5,13],"autoregressive":[6],"item-identifier":[7],"generation.":[8],"Specifically,":[9],"items":[10],"are":[11,18],"encoded":[12],"semantic":[14],"identifiers":[15],"(SIDs),":[16],"which":[17,87],"short":[19],"coarse-to-fine":[20],"token":[21],"sequences":[22],"whose":[23],"early":[24],"tokens":[25,31],"capture":[26],"broad":[27],"semantics":[28],"and":[29,42,93,157,166,178],"later":[30],"refine":[32],"them.":[33],"Recent":[34],"work":[35],"augments":[36],"this":[37,114,131],"paradigm":[38],"with":[39,48,54,100,125,186],"reasoning":[40,119,155],"traces":[41],"optimizes":[43],"them":[44],"via":[45],"reinforcement":[46],"learning":[47],"verifiable":[49],"rewards,":[50],"typically":[51],"outcome-reward":[52,84],"algorithm":[53],"exact-match":[55,65,192],"feedback":[56,66,193],"on":[57,67],"the":[58,68,74,91,101,107,144,162,187,210,215],"generated":[59,69,81],"SID.":[60],"However,":[61],"in":[62,113,133],"large-catalog":[63],"recommendation,":[64],"SID":[70,82,127,167],"only":[71,160],"reports":[72],"whether":[73],"final":[75],"item":[76],"is":[77,116],"correct;":[78],"when":[79],"a":[80,117,149],"mismatches,":[83],"cannot":[85],"identify":[86,105],"SID-token":[88,97],"caused":[90],"mismatch":[92],"may":[94],"penalize":[95],"matched":[96],"positions":[98],"together":[99],"mismatched":[102],"position.":[103],"We":[104,129],"that":[106,202],"natural":[108],"unit":[109],"of":[110,214],"credit":[111,196],"assignment":[112,197],"setting":[115],"single":[118],"step":[120,156],"(one":[121],"thinking":[122,164],"block":[123,165],"paired":[124],"one":[126,141],"token).":[128],"instantiate":[130],"idea":[132],"SAPO":[134,147,174],"(Step-Aligned":[135],"Policy":[136],"Optimization):":[137],"rather":[138],"than":[139],"broadcasting":[140],"advantage":[142,152],"to":[143,161],"whole":[145],"response,":[146],"computes":[148],"separate":[150],"group-relative":[151],"for":[153,205],"each":[154],"applies":[158],"it":[159],"corresponding":[163],"token.":[168],"Across":[169],"three":[170],"real-world":[171],"datasets,":[173],"stabilizes":[175],"reinforcement-learning":[176,203],"training":[177],"consistently":[179],"improves":[180],"over":[181],"existing":[182],"generative":[183],"baselines,":[185],"largest":[188],"gains":[189],"where":[190],"sparse":[191],"makes":[194],"reasoning-step":[195],"important.":[198],"Our":[199],"results":[200],"suggest":[201],"objectives":[204],"structured":[206],"generation":[207],"should":[208],"mirror":[209],"decoder's":[211],"own":[212],"decomposition":[213],"output.":[216]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}
