{"id":"https://openalex.org/W7169575253","doi":"https://doi.org/10.48550/arxiv.2607.14777","title":"SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning","display_name":"SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning","publication_year":2026,"publication_date":"2026-07-16","ids":{"openalex":"https://openalex.org/W7169575253","doi":"https://doi.org/10.48550/arxiv.2607.14777"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.14777","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.14777","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.2607.14777","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5141103292","display_name":"Jinyang Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Jinyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026769012","display_name":"Sen Yang","orcid":"https://orcid.org/0000-0003-1064-0055"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Shuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141092082","display_name":"Zhengxi Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Zhengxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047301622","display_name":"F Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Fan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141100569","display_name":"Yuhao Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Yuhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141061632","display_name":"Lang Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Lang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141094465","display_name":"Haoran Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Haoran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141084712","display_name":"Zheng Lian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lian, Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141057098","display_name":"Shuai Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shuai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141049550","display_name":"Zhengqi Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Zhengqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5141066904","display_name":"Jianhua Tao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tao, Jianhua","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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.6714000105857849,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.6714000105857849,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.11630000174045563,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.04340000078082085,"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/hindsight-bias","display_name":"Hindsight bias","score":0.9664999842643738},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6890000104904175},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.660099983215332},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5881999731063843},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5608000159263611},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.48080000281333923},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.4016999900341034}],"concepts":[{"id":"https://openalex.org/C10347200","wikidata":"https://www.wikidata.org/wiki/Q1960297","display_name":"Hindsight bias","level":2,"score":0.9664999842643738},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6890000104904175},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.660099983215332},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6407999992370605},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5881999731063843},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5638999938964844},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5608000159263611},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5590999722480774},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.48080000281333923},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.4016999900341034},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.30640000104904175},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C132758656","wikidata":"https://www.wikidata.org/wiki/Q5307365","display_name":"Dreyfus model of skill acquisition","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.258899986743927}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.14777","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.14777","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.2607.14777","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.14777","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":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5322204828262329}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"are":[3],"increasingly":[4],"trained":[5],"as":[6,111],"interactive":[7],"agents":[8],"for":[9],"long-horizon":[10],"tasks":[11,187],"involving":[12],"multi-turn":[13],"interaction,":[14],"tool":[15],"use,":[16],"and":[17,47,69,88,109,127,147,184,194],"environment":[18],"feedback.":[19],"Outcome-based":[20],"reinforcement":[21],"learning":[22],"(RL)":[23],"provides":[24],"a":[25,41,57,156],"practical":[26],"optimization":[27],"paradigm,":[28],"but":[29],"its":[30],"sparse":[31],"trajectory-level":[32],"rewards":[33],"offer":[34],"limited":[35],"guidance":[36],"on":[37,182],"intermediate":[38],"decisions,":[39],"leaving":[40],"supervision":[42,133,173],"gap":[43],"between":[44],"episode-level":[45],"outcomes":[46],"token-level":[48,158],"policy":[49,77,83,105],"learning.":[50],"We":[51],"propose":[52],"SEED":[53,79,139,190],"(SElf-Evolving":[54],"On-Policy":[55],"Distillation),":[56],"self-evolving":[58],"framework":[59],"that":[60,92,114,189],"converts":[61],"completed":[62,86],"on-policy":[63,159],"trajectories":[64,87,108],"into":[65,75,155],"training-time":[66],"hindsight":[67,116,132],"skills":[68,91,117],"distills":[70],"their":[71],"behavioral":[72],"effect":[73],"back":[74],"the":[76,82,103,112,137,142,151,171,176],"model.":[78],"first":[80],"fine-tunes":[81],"to":[84,134,200],"analyze":[85],"generate":[89],"natural-language":[90],"capture":[93],"reusable":[94],"workflows,":[95],"decisive":[96],"observations,":[97],"or":[98],"failure-avoidance":[99],"rules.":[100],"During":[101],"RL,":[102,169],"current":[104,177],"both":[106],"collects":[107],"serves":[110],"analyzer":[113],"extracts":[115],"from":[118],"them.":[119],"Policy":[120],"updates":[121],"therefore":[122],"improve":[123],"subsequent":[124],"decision":[125],"making":[126],"skill":[128],"analysis":[129],"together,":[130],"allowing":[131],"evolve":[135],"with":[136,167,175],"policy.":[138],"then":[140],"re-scores":[141],"sampled":[143],"actions":[144],"under":[145],"ordinary":[146],"skill-augmented":[148],"contexts,":[149],"converting":[150],"skill-induced":[152],"probability":[153],"shift":[154],"dense":[157],"distillation":[160],"signal.":[161],"This":[162],"signal":[163],"is":[164,205],"jointly":[165],"optimized":[166],"outcome-based":[168],"keeping":[170],"auxiliary":[172],"aligned":[174],"trajectory":[178],"distribution.":[179],"Extensive":[180],"experiments":[181],"text-based":[183],"vision-based":[185],"agentic":[186],"show":[188],"consistently":[191],"improves":[192],"performance":[193],"sample":[195],"efficiency,":[196],"exhibiting":[197],"robust":[198],"generalization":[199],"unseen":[201],"scenarios.":[202],"Our":[203],"code":[204],"available":[206],"at":[207],"https://github.com/jinyangwu/SEED.":[208]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-18T00:00:00"}
