{"id":"https://openalex.org/W7140148444","doi":"https://doi.org/10.48550/arxiv.2603.19470","title":"Adaptive Layerwise Perturbation: Unifying Off-Policy Corrections for LLM RL","display_name":"Adaptive Layerwise Perturbation: Unifying Off-Policy Corrections for LLM RL","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7140148444","doi":"https://doi.org/10.48550/arxiv.2603.19470"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.19470","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19470","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.2603.19470","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072672530","display_name":"Chenlu Ye","orcid":"https://orcid.org/0000-0002-0243-4449"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Chenlu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130405029","display_name":"Xuanchang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xuanchang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101645247","display_name":"Yifan Hao","orcid":"https://orcid.org/0000-0002-0304-4514"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130333000","display_name":"Zhou Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Zhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130384029","display_name":"Ziji Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ziji","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130334729","display_name":"Abhinav Gullapalli","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gullapalli, Abhinav","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130350334","display_name":"Hao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130386461","display_name":"Jing Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130338599","display_name":"Tong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Tong","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.14890000224113464,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.14890000224113464,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.14100000262260437,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.09459999948740005,"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/inference","display_name":"Inference","score":0.850600004196167},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.739300012588501},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5342000126838684},{"id":"https://openalex.org/keywords/cover","display_name":"Cover (algebra)","score":0.5234000086784363},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.4099000096321106},{"id":"https://openalex.org/keywords/indirect-inference","display_name":"Indirect Inference","score":0.35260000824928284},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3517000079154968}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.850600004196167},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.739300012588501},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.557200014591217},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5342000126838684},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.5234000086784363},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4099000096321106},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3806000053882599},{"id":"https://openalex.org/C2779793024","wikidata":"https://www.wikidata.org/wiki/Q17299941","display_name":"Indirect Inference","level":3,"score":0.35260000824928284},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3517000079154968},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.34869998693466187},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3418000042438507},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.3264000117778778},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.31690001487731934},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28139999508857727},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.27639999985694885}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.19470","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19470","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.2603.19470","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19470","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/4","display_name":"Quality Education","score":0.7031506896018982}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Off-policy":[0],"problems":[1],"such":[2],"as":[3,97],"policy":[4,51,96,108,124,132,136],"staleness":[5],"and":[6,17,29,59,91,133,155,158,178,199,223],"training--inference":[7],"mismatch":[8,141],"have":[9],"become":[10],"a":[11],"major":[12],"bottleneck":[13],"for":[14],"training":[15,167],"stability":[16],"further":[18,56,171],"exploration":[19],"in":[20,109,195],"LLM":[21],"RL.":[22],"The":[23],"distribution":[24,146],"gap":[25,151],"between":[26,152],"the":[27,35,50,64,82,93,98,101,105,110,122,130,135,144,150,153,160,196],"inference":[28,39,107,131,156],"updated":[30,123,154],"policies":[31,157],"grows":[32],"because":[33],"of":[34,86,100,162],"techniques":[36],"to":[37,42,117,138],"enhance":[38],"efficiency,":[40],"leading":[41],"heavy-tailed":[43],"importance":[44,102,163],"ratios.":[45],"Heavy-tailed":[46],"ratios":[47],"arise":[48],"when":[49],"is":[52,170],"locally":[53],"sharp,":[54],"which":[55,76],"inflates":[57],"gradients":[58],"can":[60,147],"push":[61],"updates":[62,90],"outside":[63],"trust":[65],"region.":[66],"To":[67],"address":[68],"this,":[69],"we":[70],"propose":[71],"Adaptive":[72],"Layerwise":[73],"Perturbation":[74],"(ALP),":[75],"injects":[77],"small":[78],"learnable":[79],"perturbations":[80,213],"into":[81],"input":[83],"hidden":[84],"states":[85],"each":[87],"layer":[88],"during":[89,202],"uses":[92],"resulting":[94],"perturbed":[95],"numerator":[99],"ratio":[103],"against":[104],"unchanged":[106],"objective.":[111],"Intuitively,":[112],"by":[113],"adding":[114],"controlled":[115],"noise":[116],"intermediate":[118],"representations,":[119],"ALP":[120,185],"prevents":[121],"from":[125,129],"deviating":[126],"too":[127],"sharply":[128],"enlarges":[134],"family":[137],"cover":[139],"inference-time":[140],"noise.":[142],"Hence,":[143],"flattened":[145],"naturally":[148],"tighten":[149],"reduce":[159],"tail":[161,198],"ratios,":[164],"thus":[165],"maintaining":[166],"stability.":[168],"This":[169],"validated":[172],"empirically.":[173],"Experiments":[174],"on":[175],"single-turn":[176],"math":[177],"multi-turn":[179],"tool-integrated":[180],"reasoning":[181],"tasks":[182],"show":[183,210],"that":[184,211],"not":[186],"only":[187],"improves":[188],"final":[189],"performance,":[190],"but":[191],"also":[192],"avoids":[193],"blow-up":[194],"importance-ratio":[197],"KL":[200],"spikes":[201],"iterative":[203],"training,":[204],"along":[205],"with":[206],"boosted":[207],"exploration.":[208],"Ablations":[209],"representation-level":[212],"across":[214],"all":[215],"layers":[216],"are":[217],"most":[218],"effective,":[219],"substantially":[220],"outperforming":[221],"partial-layer":[222],"logits-only":[224],"variants.":[225]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-24T00:00:00"}
