{"id":"https://openalex.org/W7166777890","doi":"https://doi.org/10.48550/arxiv.2606.30627","title":"Pessimism's Paradox: Conservative Offline Training Amplifies Reward Hacking During Online Adaptation in Reasoning Models","display_name":"Pessimism's Paradox: Conservative Offline Training Amplifies Reward Hacking During Online Adaptation in Reasoning Models","publication_year":2026,"publication_date":"2026-06-29","ids":{"openalex":"https://openalex.org/W7166777890","doi":"https://doi.org/10.48550/arxiv.2606.30627"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.30627","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30627","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.2606.30627","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025127084","display_name":"Subramanyam Sahoo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sahoo, Subramanyam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139666472","display_name":"Aman Chadha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chadha, Aman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139644547","display_name":"Vinija Jain","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jain, Vinija","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139677106","display_name":"Divya Chaudhary","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chaudhary, Divya","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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.14020000398159027,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.14020000398159027,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.11240000277757645,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.1080000028014183,"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/conservatism","display_name":"Conservatism","score":0.7016000151634216},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5544000267982483},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.47589999437332153},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.4722999930381775},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.3817000091075897},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.3646000027656555},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.34790000319480896},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3377000093460083}],"concepts":[{"id":"https://openalex.org/C96640997","wikidata":"https://www.wikidata.org/wiki/Q5163004","display_name":"Conservatism","level":3,"score":0.7016000151634216},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6521999835968018},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5544000267982483},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.536300003528595},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.47589999437332153},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.4722999930381775},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46480000019073486},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.4000000059604645},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.34790000319480896},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3377000093460083},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3059000074863434},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.29510000348091125},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.28700000047683716},{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.28029999136924744},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C86844869","wikidata":"https://www.wikidata.org/wiki/Q2798820","display_name":"Hacker","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.30627","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30627","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.2606.30627","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30627","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":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.4729524552822113}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Conservative":[0],"offline":[1,91],"training":[2,147],"is":[3,26,166],"widely":[4],"advocated":[5],"as":[6],"a":[7,15,33,46,74,120,140,175,185],"safe":[8],"foundation":[9],"for":[10],"subsequent":[11],"online":[12,72,170],"adaptation:":[13],"if":[14],"policy":[16,48,128],"stays":[17],"close":[18],"to":[19,29,178],"well-supported":[20],"behaviour,":[21],"the":[22,99,106,144,179,202],"argument":[23],"goes,":[24],"it":[25],"less":[27],"likely":[28],"exploit":[30],"imperfections":[31],"in":[32,139],"learned":[34,75],"reward":[35,76,145],"model.":[36],"We":[37,44,87,172],"challenge":[38],"this":[39,156],"intuition":[40],"empirically":[41],"and":[42,102,153,165,183],"mechanistically.":[43],"train":[45],"Qwen3-14B":[47],"under":[49,105],"Direct":[50],"Preference":[51],"Optimisation":[52],"(DPO)":[53],"with":[54,109,135,163],"three":[55,115],"levels":[56],"of":[57,143],"conservatism":[58,92,188],"($\u03b2\\in":[59],"\\{\u03b2_{\\mathrm{lo}},":[60],"\u03b2_{\\mathrm{mid}},":[61],"\u03b2_{\\mathrm{hi}}\\}$":[62],"derived":[63],"from":[64],"empirical":[65],"log-ratio":[66],"percentiles),":[67],"then":[68],"adapt":[69],"each":[70],"checkpoint":[71],"against":[73,195],"ensemble":[77,158],"(3\\,$\\times$\\,Qwen3-1.7B)":[78],"while":[79],"measuring":[80],"true":[81],"performance":[82],"on":[83],"GSM8K":[84],"exact-answer":[85],"accuracy.":[86],"find":[88],"that":[89,191,201],"\\emph{higher":[90],"monotonically":[93],"increases":[94,162],"reward-hacking":[95],"damage},":[96],"measured":[97],"by":[98],"Goodhart":[100],"gap":[101],"its":[103],"area":[104],"curve":[107,177],"(AUGC),":[108],"Spearman":[110],"$\u03c1=":[111],"1.0$":[112],"across":[113],"all":[114],"conditions.":[116],"Mechanistic":[117],"analysis":[118],"reveals":[119],"three-link":[121],"causal":[122],"chain:":[123],"(i)":[124],"high-$\u03b2$":[125],"DPO":[126],"compresses":[127],"entropy,":[129],"(ii)":[130],"Low-entropy":[131],"policies":[132],"generate":[133],"responses":[134],"reduced":[136],"diversity,":[137],"concentrating":[138],"narrow":[141],"region":[142],"model's":[146],"distribution":[148],"(lower":[149],"pairwise":[150],"cosine":[151],"distance),":[152],"(iii)":[154],"despite":[155],"proximity,":[157],"disagreement":[159],"(epistemic":[160],"uncertainty)":[161],"$\u03b2$":[164],"exploited":[167],"faster":[168],"during":[169],"optimisation.":[171],"further":[173],"fit":[174],"power-law":[176],"$(\u03b2,":[180],"\\augc)$":[181],"data":[182],"identify":[184],"practical":[186],"optimal":[187],"level":[189],"$\u03b2^{\\star}$":[190],"balances":[192],"alignment":[193],"fidelity":[194],"hacking":[196],"vulnerability.":[197],"Our":[198],"results":[199],"suggest":[200],"field":[203],"needs":[204],"\\emph{calibrated},":[205],"not":[206],"\\emph{maximal},":[207],"conservatism.":[208]},"counts_by_year":[],"updated_date":"2026-07-01T06:29:00.853634","created_date":"2026-07-01T00:00:00"}