{"id":"https://openalex.org/W7162769745","doi":"https://doi.org/10.48550/arxiv.2605.29888","title":"LaRA: Layer-wise Representation Analysis for Detecting Data Contamination in RL Post-Training","display_name":"LaRA: Layer-wise Representation Analysis for Detecting Data Contamination in RL Post-Training","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162769745","doi":"https://doi.org/10.48550/arxiv.2605.29888"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.29888","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29888","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.2605.29888","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120050218","display_name":"Minju Gwak","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gwak, Minju","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124248138","display_name":"Minseo Kwak","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kwak, Minseo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137382675","display_name":"Dongseok Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Dongseok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137385031","display_name":"Guijin Son","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Son, Guijin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137399210","display_name":"Alan Ritter","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ritter, Alan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137370133","display_name":"Jaehyung Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Jaehyung","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/T10028","display_name":"Topic Modeling","score":0.1420000046491623,"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/T10028","display_name":"Topic Modeling","score":0.1420000046491623,"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":0.11410000175237656,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.10610000044107437,"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/contamination","display_name":"Contamination","score":0.553600013256073},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5041000247001648},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4936999976634979},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.45840001106262207},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.44830000400543213},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.4219000041484833}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6444000005722046},{"id":"https://openalex.org/C112570922","wikidata":"https://www.wikidata.org/wiki/Q60528603","display_name":"Contamination","level":2,"score":0.553600013256073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.525600016117096},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5041000247001648},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4936999976634979},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.45840001106262207},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.44830000400543213},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.4219000041484833},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4066999852657318},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.4050999879837036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37560001015663147},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.26100000739097595},{"id":"https://openalex.org/C160343418","wikidata":"https://www.wikidata.org/wiki/Q185256","display_name":"Rigidity (electromagnetism)","level":2,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.29888","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29888","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.2605.29888","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29888","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":{"Reinforcement":[0],"learning":[1],"(RL)":[2],"post-training":[3],"has":[4,16],"shown":[5],"to":[6],"improve":[7],"reasoning":[8,145],"in":[9,26,81],"large":[10],"language":[11],"models":[12,58,146],"(LLMs).":[13],"However,":[14],"there":[15],"been":[17],"little":[18],"exploration":[19],"on":[20,45,124,143],"the":[21,36],"problem":[22],"of":[23,35],"data":[24],"contamination":[25,80,105,131,156],"RL":[27,60,82],"post-training,":[28],"potentially":[29],"undermining":[30],"generalization":[31],"and":[32,95,119,140],"evaluation":[33],"reliability":[34],"training":[37],"process":[38],"itself.":[39],"Existing":[40],"detection":[41,132],"methods":[42],"primarily":[43],"rely":[44],"output-level":[46,153],"signals":[47],"such":[48],"as":[49],"likelihood":[50],"or":[51],"entropy,":[52],"which":[53],"become":[54],"unreliable":[55],"for":[56,78,155],"RL-trained":[57,144],"since":[59],"shapes":[61],"behavior":[62],"through":[63],"trajectory-level":[64],"rewards":[65],"rather":[66],"than":[67],"token":[68],"likelihoods.":[69],"We":[70,102],"propose":[71],"LaRA,":[72],"a":[73,130],"layer-wise":[74],"representation":[75,97],"analysis":[76],"framework":[77],"detecting":[79],"post-trained":[83],"LLMs.":[84],"LaRA":[85],"introduces":[86],"three":[87],"complementary":[88],"metrics,":[89],"measuring":[90],"perturbation":[91,114],"sensitivity,":[92,115],"directional":[93,117],"collapse,":[94,118],"local":[96,121],"rigidity":[98],"under":[99],"controlled":[100],"perturbations.":[101],"find":[103],"that":[104,134,148],"produces":[106],"progressive":[107],"geometric":[108],"deviations":[109,137],"across":[110,138],"layers,":[111],"including":[112],"amplified":[113],"stronger":[116],"enhanced":[120],"rigidity.":[122],"Based":[123],"our":[125,149],"findings,":[126],"we":[127],"also":[128],"develop":[129],"protocol":[133,150],"aggregates":[135],"representation-level":[136],"layers":[139],"metrics.":[141],"Experiments":[142],"show":[147],"outperforms":[151],"existing":[152],"baselines":[154],"detection.":[157]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}