{"id":"https://openalex.org/W7164827814","doi":"https://doi.org/10.48550/arxiv.2606.14694","title":"AdaSR: Adaptive Streaming Reasoning with Hierarchical Relative Policy Optimization","display_name":"AdaSR: Adaptive Streaming Reasoning with Hierarchical Relative Policy Optimization","publication_year":2026,"publication_date":"2026-06-12","ids":{"openalex":"https://openalex.org/W7164827814","doi":"https://doi.org/10.48550/arxiv.2606.14694"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.14694","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14694","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":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.2606.14694","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138655643","display_name":"Junlong Tong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tong, Junlong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138671735","display_name":"Wenqi Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Wenqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138671662","display_name":"Yingqi Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Yingqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138642174","display_name":"Anhao Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Anhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138682478","display_name":"Xuan Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Xuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138689700","display_name":"Yang Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138684320","display_name":"Xiaoyu Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Xiaoyu","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.20190000534057617,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.20190000534057617,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.20020000636577606,"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":0.07079999893903732,"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/computation","display_name":"Computation","score":0.49939998984336853},{"id":"https://openalex.org/keywords/reasoning-system","display_name":"Reasoning system","score":0.41679999232292175},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3986999988555908},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.3901999890804291},{"id":"https://openalex.org/keywords/streaming-data","display_name":"Streaming data","score":0.37220001220703125},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.29580000042915344},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.28290000557899475}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8136000037193298},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5092999935150146},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.49939998984336853},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3986999988555908},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.3901999890804291},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3767000138759613},{"id":"https://openalex.org/C2777611316","wikidata":"https://www.wikidata.org/wiki/Q39045282","display_name":"Streaming data","level":2,"score":0.37220001220703125},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C107848011","wikidata":"https://www.wikidata.org/wiki/Q4680756","display_name":"Adaptive reasoning","level":4,"score":0.2743000090122223},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.2653999924659729},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.14694","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14694","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":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.2606.14694","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.14694","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":null,"license_id":null,"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","score":0.46504417061805725,"display_name":"Quality Education"}],"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],"reasoning":[1,55,86,122,137,140,169,189],"models":[2,44,58,90],"typically":[3],"follow":[4],"a":[5,15,40,151,185],"read-then-think":[6],"paradigm:":[7],"they":[8,64],"observe":[9],"the":[10,21,101],"complete":[11],"input,":[12],"reason":[13,92],"over":[14,155],"static":[16],"context,":[17],"and":[18,33,43,48,96,109,138,162,175,193],"then":[19],"produce":[20],"answer.":[22],"Yet":[23],"many":[24],"real-world":[25],"scenarios":[26],"are":[27],"inherently":[28],"dynamic,":[29],"such":[30],"as":[31,39],"audio":[32],"video":[34],"stream,":[35],"where":[36],"information":[37],"arrives":[38],"continuous":[41],"stream":[42,102],"must":[45],"reason,":[46],"update,":[47],"respond":[49],"under":[50],"partial":[51],"observations.":[52],"Recent":[53],"streaming":[54,85,95,136,194],"methods":[56],"allow":[57],"to":[59,91,107,113,166],"think":[60],"while":[61],"reading,":[62],"but":[63],"largely":[65],"rely":[66],"on":[67],"supervised":[68,198],"imitation":[69],"of":[70,148],"pre-constructed":[71],"trajectories,":[72],"which":[73,131],"limits":[74],"their":[75],"flexibility.":[76],"In":[77],"this":[78,120],"paper,":[79],"we":[80,124],"propose":[81],"AdaSR,":[82],"an":[83],"adaptive":[84,163],"framework":[87],"that":[88,182],"enables":[89],"during":[93],"input":[94],"perform":[97],"final":[98,172],"deliberation":[99],"once":[100],"is":[103],"complete,":[104],"learning":[105],"when":[106],"think,":[108],"how":[110],"much":[111],"computation":[112,178],"allocate":[114],"across":[115],"different":[116],"stages.":[117],"To":[118],"optimize":[119],"hierarchical":[121],"process,":[123],"introduce":[125],"Hierarchical":[126],"Relative":[127],"Policy":[128],"Optimization":[129],"(HRPO),":[130],"decomposes":[132],"policy":[133],"optimization":[134],"into":[135],"deep":[139],"phases,":[141],"providing":[142],"more":[143],"fine-grained":[144],"advantage":[145,154],"assignment":[146],"instead":[147],"uniformly":[149],"distributing":[150],"single":[152],"sequence-level":[153],"all":[156],"tokens.":[157],"HRPO":[158],"integrates":[159],"format,":[160],"accuracy,":[161,190],"thinking":[164],"rewards":[165],"enforce":[167],"valid":[168],"protocols,":[170],"preserve":[171],"task":[173],"performance,":[174],"encourage":[176],"latency-aware":[177],"allocation.":[179],"Experiments":[180],"show":[181],"AdaSR":[183],"achieves":[184],"better":[186],"balance":[187],"among":[188],"computational":[191],"efficiency,":[192],"latency":[195],"compared":[196],"with":[197],"fine-tuning":[199],"baseline.":[200],"We":[201],"release":[202],"our":[203],"code":[204],"at":[205],"https://github.com/EIT-NLP/StreamingLLM/tree/main/AdaSR.":[206]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-16T00:00:00"}
