{"id":"https://openalex.org/W7160629518","doi":"https://doi.org/10.48550/arxiv.2605.05922","title":"Think, then Score: Decoupled Reasoning and Scoring for Video Reward Modeling","display_name":"Think, then Score: Decoupled Reasoning and Scoring for Video Reward Modeling","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160629518","doi":"https://doi.org/10.48550/arxiv.2605.05922"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.05922","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05922","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.2605.05922","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135698624","display_name":"Yuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067586474","display_name":"Ouxiang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ouxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135664527","display_name":"Yulong Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Yulong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078282320","display_name":"Borui Liao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Borui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135673347","display_name":"Jiajun Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Jiajun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135708311","display_name":"Jinghan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jinghan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135686943","display_name":"Meng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Meng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135694724","display_name":"Xintao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xintao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135693817","display_name":"Pengfei Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wan, Pengfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135722541","display_name":"Kuien Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Kuien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135667887","display_name":"Xiang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xiang","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":0.335099995136261,"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":0.335099995136261,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.17350000143051147,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.14229999482631683,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.886900007724762},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6876999735832214},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6168000102043152},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5828999876976013},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5516999959945679},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4645000100135803},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3564999997615814},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.32580000162124634}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.886900007724762},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7757999897003174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6894000172615051},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6876999735832214},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6312999725341797},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6168000102043152},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5828999876976013},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5516999959945679},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4645000100135803},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3564999997615814},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.29319998621940613},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.28360000252723694},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.25850000977516174}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.05922","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05922","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.2605.05922","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05922","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":[{"display_name":"Reduced inequalities","score":0.7603962421417236,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2],"generative":[3],"video":[4,23,155],"models":[5,25,62],"are":[6],"increasingly":[7],"driven":[8],"by":[9,58,172],"post-training":[10],"and":[11,73,93,122,146,153,184,215,228],"test-time":[12],"scaling,":[13],"both":[14],"of":[15,22,120,135,143,179],"which":[16],"critically":[17],"depend":[18],"on":[19,55,76],"the":[20,104,118,132,140,190,230],"quality":[21,227],"reward":[24,29,156],"(RMs).":[26],"An":[27],"ideal":[28],"model":[30,241],"should":[31],"predict":[32],"accurate":[33],"rewards":[34,53],"that":[35,188,222,234],"align":[36],"with":[37,86],"human":[38,107],"preferences":[39],"across":[40],"diverse":[41],"scenarios.":[42],"However,":[43,109],"existing":[44],"paradigms":[45],"face":[46],"a":[47,125,151,160,173,180,185,197,201,206,217],"fundamental":[48],"dilemma:":[49],"\\textit{Discriminative":[50],"RMs}":[51,85],"regress":[52],"directly":[54,237],"features":[56],"extracted":[57],"multimodal":[59],"large":[60],"language":[61],"(MLLMs)":[63],"without":[64],"explicit":[65,169],"reasoning,":[66],"making":[67],"them":[68],"prone":[69],"to":[70,102,117,210,239],"shortcut":[71],"learning":[72,220],"heavily":[74],"reliant":[75],"massive":[77],"data":[78],"scaling":[79],"for":[80],"generalization.":[81],"In":[82],"contrast,":[83],"\\textit{Generative":[84],"Chain-of-Thought":[87],"(CoT)":[88],"reasoning":[89,121,137,145,226,236],"exhibit":[90],"superior":[91,240],"interpretability":[92],"generalization":[94,133],"potential,":[95],"as":[96],"they":[97,110],"leverage":[98],"fine-grained":[99],"semantic":[100],"supervision":[101],"internalize":[103],"rationales":[105],"behind":[106],"preferences.":[108],"suffer":[111],"from":[112],"inherent":[113],"optimization":[114],"bottlenecks":[115],"due":[116],"coupling":[119],"scoring":[123,176,213],"within":[124],"single":[126],"autoregressive":[127],"inference":[128],"chain.":[129],"To":[130],"harness":[131],"benefits":[134],"CoT":[136,225],"while":[138],"mitigating":[139],"training":[141],"instability":[142],"coupled":[144],"scoring,":[147],"we":[148],"introduce":[149],"DeScore,":[150],"training-efficient":[152],"generalizable":[154],"model.":[157],"DeScore":[158,193],"employs":[159],"decoupled":[161],"``think-then-score''":[162],"paradigm:":[163],"an":[164,168],"MLLM":[165],"first":[166],"generates":[167],"CoT,":[170],"followed":[171],"dedicated":[174],"discriminative":[175,202],"module":[177],"consisting":[178],"learnable":[181],"query":[182],"token":[183],"regression":[186],"head":[187],"predicts":[189],"final":[191,231],"reward.":[192],"is":[194],"optimized":[195],"via":[196],"two-stage":[198],"framework:":[199],"(1)":[200],"cold":[203],"start":[204],"incorporating":[205],"random":[207],"mask":[208],"mechanism":[209],"ensure":[211],"robust":[212],"capabilities,":[214],"(2)":[216],"dual-objective":[218],"reinforcement":[219],"stage":[221],"independently":[223],"refines":[224],"calibrates":[229],"reward,":[232],"ensuring":[233],"higher-quality":[235],"translates":[238],"performance.":[242]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}