{"id":"https://openalex.org/W7128705952","doi":"https://doi.org/10.48550/arxiv.2602.10437","title":"Control Reinforcement Learning: Interpretable Token-Level Steering of LLMs via Sparse Autoencoder Features","display_name":"Control Reinforcement Learning: Interpretable Token-Level Steering of LLMs via Sparse Autoencoder Features","publication_year":2026,"publication_date":"2026-02-11","ids":{"openalex":"https://openalex.org/W7128705952","doi":"https://doi.org/10.48550/arxiv.2602.10437"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.10437","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.10437","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":"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.2602.10437","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111062496","display_name":"Seonglae Cho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cho, Seonglae","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125476661","display_name":"Zekun Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zekun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125751909","display_name":"Adriano Koshiyama","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koshiyama, Adriano","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":0.707099974155426,"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":0.707099974155426,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.04430000111460686,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.040800001472234726,"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/interpretability","display_name":"Interpretability","score":0.7506999969482422},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7300999760627747},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6866000294685364},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6279000043869019},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5853000283241272},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.38429999351501465},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3734999895095825},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.37070000171661377}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7506999969482422},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7300999760627747},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6998999714851379},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6866000294685364},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6507999897003174},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6279000043869019},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5853000283241272},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.459199994802475},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.38429999351501465},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3734999895095825},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.34150001406669617},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C2778744346","wikidata":"https://www.wikidata.org/wiki/Q1152224","display_name":"Distinctive feature","level":2,"score":0.25940001010894775}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.10437","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.10437","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":"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.2602.10437","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.10437","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":"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":0.49857452511787415,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Sparse":[0],"autoencoders":[1],"(SAEs)":[2],"decompose":[3],"language":[4],"model":[5,21,55],"activations":[6],"into":[7],"interpretable":[8,45],"features,":[9],"but":[10],"existing":[11],"methods":[12],"reveal":[13],"only":[14],"which":[15,19,31],"features":[16,38,52,101,107],"activate,":[17],"not":[18],"change":[20,54],"outputs":[22,56],"when":[23,57],"amplified.":[24,58],"We":[25],"introduce":[26],"Control":[27],"Reinforcement":[28],"Learning":[29],"(CRL),":[30],"trains":[32],"a":[33,137],"policy":[34,50,91],"to":[35],"select":[36],"SAE":[37],"for":[39],"steering":[40,135],"at":[41],"each":[42],"token,":[43],"producing":[44],"intervention":[46,128,148],"logs:":[47],"the":[48],"learned":[49,133],"identifies":[51],"that":[53,141],"Adaptive":[59],"Feature":[60],"Masking":[61],"encourages":[62],"diverse":[63],"feature":[64,82,134,144],"discovery":[65],"while":[66,125],"preserving":[67],"singlefeature":[68],"interpretability.":[69],"The":[70],"framework":[71],"yields":[72],"new":[73],"analysis":[74,89,145],"capabilities:":[75],"branch":[76],"point":[77],"tracking":[78],"locates":[79],"tokens":[80],"where":[81],"choice":[83],"determines":[84],"output":[85],"correctness;":[86],"critic":[87],"trajectory":[88],"separates":[90],"limitations":[92],"from":[93],"value":[94],"estimation":[95],"errors;":[96],"layer-wise":[97],"comparison":[98],"reveals":[99],"syntactic":[100],"in":[102,108],"early":[103],"layers":[104],"and":[105,120],"semantic":[106],"later":[109],"layers.":[110],"On":[111],"Gemma":[112],"2":[113],"2B":[114],"across":[115],"MMLU,":[116],"BBQ,":[117],"GSM8K,":[118],"HarmBench,":[119],"XSTest,":[121],"CRL":[122],"achieves":[123],"improvements":[124],"providing":[126],"per-token":[127],"logs.":[129],"These":[130],"results":[131],"establish":[132],"as":[136],"mechanistic":[138],"interpretability":[139],"tool":[140],"complements":[142],"static":[143],"with":[146],"dynamic":[147],"probes":[149]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-02-13T00:00:00"}