{"id":"https://openalex.org/W7163995178","doi":"https://doi.org/10.48550/arxiv.2606.09078","title":"The Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning","display_name":"The Hidden Bias of Process Reward Models:PRISM for Rewarding the Right Reasoning","publication_year":2026,"publication_date":"2026-06-08","ids":{"openalex":"https://openalex.org/W7163995178","doi":"https://doi.org/10.48550/arxiv.2606.09078"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.09078","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09078","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.2606.09078","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012625793","display_name":"Aakriti Agrawal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agrawal, Aakriti","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125701885","display_name":"Souradip Chakraborty","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chakraborty, Souradip","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114368335","display_name":"Armin Saghafian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saghafian, Armin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062151655","display_name":"Nihal Sharma","orcid":"https://orcid.org/0000-0003-1289-5852"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sharma, Nihal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005784479","display_name":"Rizal Fathony","orcid":"https://orcid.org/0000-0003-1538-9090"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fathony, Rizal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126692828","display_name":"Nam H. Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Nam H","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138226732","display_name":"C. Bayan Bruss","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bruss, C. Bayan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130770773","display_name":"Amrit Singh Bedi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bedi, Amrit Singh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138250502","display_name":"Furong Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Furong","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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.28380000591278076,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.28380000591278076,"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.09510000050067902,"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/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.0357000008225441,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.767799973487854},{"id":"https://openalex.org/keywords/false-positives-and-false-negatives","display_name":"False positives and false negatives","score":0.5906000137329102},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5871000289916992},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5679000020027161},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.49720001220703125},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.42239999771118164},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4189999997615814},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.3531000018119812}],"concepts":[{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.767799973487854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6452999711036682},{"id":"https://openalex.org/C112789634","wikidata":"https://www.wikidata.org/wiki/Q18207010","display_name":"False positives and false negatives","level":3,"score":0.5906000137329102},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5871000289916992},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5679000020027161},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5264999866485596},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.49720001220703125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47110000252723694},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.42239999771118164},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4189999997615814},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C67666897","wikidata":"https://www.wikidata.org/wiki/Q165896","display_name":"Prism","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2809999883174896},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.27549999952316284},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C2989486834","wikidata":"https://www.wikidata.org/wiki/Q3808900","display_name":"True positive rate","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.25220000743865967},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.09078","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09078","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.2606.09078","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09078","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":[{"score":0.6941677927970886,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Process":[0],"Reward":[1],"Models":[2],"(PRMs)":[3],"improve":[4],"credit":[5],"assignment":[6],"for":[7,102,184,189,211],"reasoning":[8,210],"by":[9,22,121],"providing":[10],"step-level":[11,26,115],"feedback.":[12],"However,":[13],"we":[14,97],"identify":[15],"a":[16,106,122,134],"hidden":[17],"bias":[18],"in":[19,25],"PRMs":[20,36],"caused":[21],"severe":[23],"imbalance":[24],"training":[27,31,83,109],"data.":[28],"Standard":[29],"cross-entropy":[30],"amplifies":[32],"this":[33],"bias,":[34],"causing":[35],"to":[37,90,137,165,182],"overcredit":[38],"plausible":[39],"but":[40,205],"incorrect":[41],"steps":[42],"and":[43,73,117,145,155,168,174,187,191],"produce":[44],"high":[45,203],"false-positive":[46],"rates.":[47],"We":[48,131],"show":[49],"that":[50,81,111],"these":[51],"false":[52,59,65,150],"positives":[53,66,151],"have":[54],"an":[55],"asymmetric":[56],"downstream":[57],"effect:":[58],"negatives":[60,119],"mainly":[61],"slow":[62],"exploration,":[63],"whereas":[64],"actively":[67],"steer":[68],"Best-of-N":[69,175],"selection,":[70,176],"guided":[71,172,185],"decoding,":[72],"policy":[74,166],"optimization":[75,167],"toward":[76],"flawed":[77],"reasoning.":[78],"This":[79],"suggests":[80],"PRM":[82,108],"should":[84],"shift":[85],"from":[86,113],"pointwise":[87],"label":[88],"fitting":[89],"reliable":[91],"relative":[92],"comparisons.":[93],"To":[94],"address":[95],"this,":[96],"propose":[98],"PRISM":[99,147],"(Precision":[100],"Ranking":[101],"Improved":[103],"Step":[104],"Modeling),":[105],"policy-aware":[107],"framework":[110],"learns":[112],"contrastive":[114,140],"comparisons":[116],"hard":[118],"generated":[120],"temporal":[123],"lookahead":[124],"strategy,":[125],"requiring":[126],"no":[127],"new":[128],"human":[129],"labels.":[130],"further":[132],"use":[133],"difficulty-aware":[135],"curriculum":[136],"optimize":[138],"the":[139,208,212],"step":[141],"margin.":[142],"Across":[143],"PRMBench":[144],"ProcessBench,":[146],"substantially":[148],"reduces":[149],"(22%":[152],"on":[153],"PRMBench)":[154],"improves":[156,179],"macro":[157],"F1":[158],"over":[159],"strong":[160],"discriminative":[161],"PRMs.":[162],"When":[163],"applied":[164],"search":[169],"tasks,":[170],"including":[171],"decoding":[173,186],"it":[177],"consistently":[178],"accuracy":[180],"(up":[181],"22%":[183],"33%":[188],"Best-of-N)":[190],"robustness.":[192],"More":[193],"broadly,":[194],"trustworthy":[195],"process":[196],"supervision":[197],"is":[198],"not":[199],"just":[200],"about":[201,206],"assigning":[202],"rewards,":[204],"rewarding":[207],"right":[209,213],"reasons.":[214]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}
