{"id":"https://openalex.org/W7160502899","doi":"https://doi.org/10.48550/arxiv.2605.04712","title":"SPHERE: Mitigating the Loss of Spectral Plasticity in Mixture-of-Experts for Deep Reinforcement Learning","display_name":"SPHERE: Mitigating the Loss of Spectral Plasticity in Mixture-of-Experts for Deep Reinforcement Learning","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160502899","doi":"https://doi.org/10.48550/arxiv.2605.04712"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.04712","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04712","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.04712","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127962369","display_name":"Lirui Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Luo, Lirui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135561609","display_name":"Guoxi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Guoxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135589065","display_name":"Hongming Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Hongming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135614712","display_name":"Cong Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Cong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135551629","display_name":"Qing Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5127962369"],"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":0.35910001397132874,"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":0.35910001397132874,"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":0.2583000063896179,"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":0.050200000405311584,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.6754000186920166},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5042999982833862},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.4645000100135803},{"id":"https://openalex.org/keywords/plasticity","display_name":"Plasticity","score":0.4320000112056732},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.3799000084400177},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.3774999976158142}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6754000186920166},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6047000288963318},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5475000143051147},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5042999982833862},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.4645000100135803},{"id":"https://openalex.org/C79186407","wikidata":"https://www.wikidata.org/wiki/Q472074","display_name":"Plasticity","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.3799000084400177},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3774999976158142},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3443000018596649},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.34130001068115234},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C22033958","wikidata":"https://www.wikidata.org/wiki/Q7167036","display_name":"Perceptual learning","level":3,"score":0.2653000056743622},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2619999945163727},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.04712","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04712","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.04712","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04712","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.4296336770057678}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,14],"deep":[1],"reinforcement":[2,59],"learning":[3,17,52,60,67],"(DRL),":[4],"an":[5,153],"agent":[6],"is":[7],"trained":[8],"from":[9,23,32],"a":[10,15,70,93,101,122],"stream":[11],"of":[12,53,72,95,111,134],"experience.":[13],"continual":[16,58,146],"setting,":[18],"such":[19],"agents":[20],"can":[21,64],"suffer":[22],"plasticity":[24,87,161],"loss:":[25],"their":[26,62],"ability":[27],"to":[28,45],"learn":[29],"new":[30,33],"skills":[31],"experiences":[34],"diminishes":[35],"over":[36,152],"training.":[37,163],"Recently,":[38],"Mixture-of-Experts":[39],"(MoE)":[40],"networks":[41],"have":[42],"been":[43],"reported":[44],"enable":[46],"scaling":[47],"laws":[48],"and":[49,139,150],"facilitate":[50],"the":[51,86,132],"diverse":[54],"skills.":[55],"However,":[56],"in":[57,89,109],"settings,":[61],"performance":[63],"degenerate":[65],"as":[66,92],"proceeds,":[68],"indicating":[69],"loss":[71,88,94,133],"plasticity.":[73,97,136],"To":[74],"address":[75],"this,":[76],"building":[77],"on":[78],"Neural":[79],"Tangent":[80],"Kernel":[81],"(NTK)":[82],"theory,":[83],"we":[84,119],"formalize":[85],"MoE":[90,155],"policies":[91,129],"spectral":[96,105,135,160],"We":[98],"then":[99],"derive":[100],"tractable":[102],"proxy":[103],"for":[104,127],"plasticity,":[106],"one":[107],"expressible":[108],"terms":[110],"individual":[112],"expert":[113],"feature":[114],"matrices.":[115],"Leveraging":[116],"this":[117],"proxy,":[118],"introduce":[120],"SPHERE,":[121],"practical":[123],"Parseval":[124],"penalty":[125],"tailored":[126],"MoE-based":[128],"that":[130],"alleviates":[131],"On":[137],"MetaWorld":[138],"HumanoidBench,":[140],"SPHERE":[141],"improves":[142],"average":[143],"success":[144],"under":[145],"RL":[147],"by":[148],"133%":[149],"50%":[151],"unregularized":[154],"baseline,":[156],"while":[157],"maintaining":[158],"higher":[159],"throughout":[162]},"counts_by_year":[],"updated_date":"2026-05-08T13:18:25.657630","created_date":"2026-05-08T00:00:00"}
