{"id":"https://openalex.org/W7148901865","doi":"https://doi.org/10.48550/arxiv.2604.01476","title":"When Reward Hacking Rebounds: Understanding and Mitigating It with Representation-Level Signals","display_name":"When Reward Hacking Rebounds: Understanding and Mitigating It with Representation-Level Signals","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148901865","doi":"https://doi.org/10.48550/arxiv.2604.01476"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.01476","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01476","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2604.01476","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132886287","display_name":"Rui Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132868652","display_name":"Ruixiang Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Ruixiang","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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.3100999891757965,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.3100999891757965,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":0.1889999955892563,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10260","display_name":"Software Engineering Research","score":0.058400001376867294,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hacker","display_name":"Hacker","score":0.8108000159263611},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6638000011444092},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6000999808311462},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5019000172615051},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.4675999879837036},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.42719998955726624},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.40880000591278076},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4059000015258789}],"concepts":[{"id":"https://openalex.org/C86844869","wikidata":"https://www.wikidata.org/wiki/Q2798820","display_name":"Hacker","level":2,"score":0.8108000159263611},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7630000114440918},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6638000011444092},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6000999808311462},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5019000172615051},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47699999809265137},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.4675999879837036},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.43630000948905945},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.42719998955726624},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.40880000591278076},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4059000015258789},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.39820000529289246},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3903999924659729},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.38519999384880066},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3587000072002411},{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.2736999988555908},{"id":"https://openalex.org/C33435437","wikidata":"https://www.wikidata.org/wiki/Q366791","display_name":"Curiosity","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C2779267917","wikidata":"https://www.wikidata.org/wiki/Q170028","display_name":"Deception","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2583000063896179},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.01476","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01476","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2604.01476","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01476","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"score":0.5288375020027161,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reinforcement":[0],"learning":[1],"for":[2,110,137],"LLMs":[3],"is":[4,166],"vulnerable":[5],"to":[6,13,39,65,86,156],"reward":[7,15,91],"hacking,":[8],"where":[9,33],"models":[10,34,62],"exploit":[11],"shortcuts":[12],"maximize":[14],"without":[16,43],"solving":[17,44],"the":[18,45,67,123,164,169],"intended":[19],"task.":[20],"We":[21],"systematically":[22],"study":[23],"this":[24,141],"phenomenon":[25],"in":[26],"coding":[27],"tasks":[28],"using":[29],"an":[30,133],"environment-manipulation":[31],"setting,":[32],"can":[35],"rewrite":[36,66],"evaluator":[37,68],"code":[38],"trivially":[40],"pass":[41],"tests":[42],"task,":[46],"as":[47,71],"a":[48,57],"controlled":[49],"testbed.":[50],"Across":[51],"both":[52],"studied":[53],"models,":[54],"we":[55,106,143],"identify":[56],"reproducible":[58],"three-phase":[59],"rebound":[60,95],"pattern:":[61],"first":[63],"attempt":[64],"but":[69],"fail,":[70],"their":[72,77],"rewrites":[73],"embed":[74],"test":[75],"cases":[76],"own":[78],"solutions":[79],"cannot":[80],"pass.":[81],"They":[82],"then":[83],"temporarily":[84],"retreat":[85],"legitimate":[87,90],"solving.":[88],"When":[89],"remains":[92],"scarce,":[93],"they":[94],"into":[96,152,168],"successful":[97],"hacking":[98,127,158,186],"with":[99,188],"qualitatively":[100],"different":[101],"strategies.":[102],"Using":[103],"representation":[104],"engineering,":[105],"extract":[107],"concept":[108,150],"directions":[109],"shortcut,":[111],"deception,":[112],"and":[113,120],"evaluation":[114],"awareness":[115],"from":[116],"domain-general":[117],"contrastive":[118],"pairs":[119],"find":[121],"that":[122],"shortcut":[124,149],"direction":[125],"tracks":[126],"behavior":[128],"most":[129],"closely,":[130],"making":[131],"it":[132],"effective":[134],"representational":[135],"proxy":[136],"detection.":[138],"Motivated":[139],"by":[140],"finding,":[142],"propose":[144],"Advantage":[145,179],"Modification,":[146],"which":[147],"integrates":[148],"scores":[151],"GRPO":[153],"advantage":[154],"computation":[155],"penalize":[157],"rollouts":[159],"before":[160],"policy":[161],"updates.":[162],"Because":[163],"penalty":[165],"internalized":[167],"training":[170],"signal":[171],"rather":[172],"than":[173],"applied":[174],"only":[175],"at":[176],"inference":[177],"time,":[178],"Modification":[180],"provides":[181],"more":[182],"robust":[183],"suppression":[184],"of":[185],"compared":[187],"generation-time":[189],"activation":[190],"steering.":[191]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-04T00:00:00"}