{"id":"https://openalex.org/W7162429167","doi":"https://doi.org/10.48550/arxiv.2605.25381","title":"Not only where, But when: Temporal Scheduling for RLVR","display_name":"Not only where, But when: Temporal Scheduling for RLVR","publication_year":2026,"publication_date":"2026-05-25","ids":{"openalex":"https://openalex.org/W7162429167","doi":"https://doi.org/10.48550/arxiv.2605.25381"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.25381","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25381","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.2605.25381","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137061416","display_name":"Jinghao Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jinghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137028144","display_name":"Ruilin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ruilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137066231","display_name":"Feng Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Feng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137047614","display_name":"Jiaqi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiaqi","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/T10028","display_name":"Topic Modeling","score":0.26969999074935913,"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/T10028","display_name":"Topic Modeling","score":0.26969999074935913,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.11029999703168869,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.05739999935030937,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.6643000245094299},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5742999911308289},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.5008999705314636},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.3693999946117401},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.3580999970436096},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.35260000824928284}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.733299970626831},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6643000245094299},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5742999911308289},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5008999705314636},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.412200003862381},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2897000014781952},{"id":"https://openalex.org/C55416958","wikidata":"https://www.wikidata.org/wiki/Q6206757","display_name":"Job shop scheduling","level":3,"score":0.28850001096725464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28630000352859497},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.258899986743927},{"id":"https://openalex.org/C180518391","wikidata":"https://www.wikidata.org/wiki/Q355217","display_name":"Time allocation","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.25381","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25381","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.2605.25381","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25381","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reinforcement":[0],"learning":[1,80,135],"with":[2,119,155],"verifiable":[3],"rewards":[4],"(RLVR)":[5],"has":[6],"become":[7],"a":[8,28,145,196],"core":[9],"technique":[10],"for":[11,148],"post-training":[12],"of":[13,109],"Large":[14],"Language":[15],"Models":[16],"(LLMs).":[17],"While":[18],"policy":[19,35,71,121,150,166,178],"optimization":[20,128,163,198],"is":[21],"driven":[22],"by":[23,49],"all":[24],"sampled":[25],"tokens":[26,117],"under":[27],"globally":[29],"broadcast":[30],"scalar":[31],"reward,":[32],"the":[33,61,97,102,107],"heterogeneous":[34,171],"behaviors":[36],"exhibited":[37],"along":[38],"trajectories":[39],"are":[40,64,82,91],"largely":[41],"overlooked":[42],"without":[43],"differentiation.":[44],"Existing":[45],"works":[46,153],"address":[47],"this":[48,74],"credit":[50,103],"allocation,":[51],"including":[52],"token-level":[53],"advantage":[54],"reweighting,":[55],"and":[56,95,123,133,152,184],"selective":[57],"token":[58],"optimization,":[59],"however,":[60],"allocation":[62,104],"criterion":[63],"principally":[65],"stagnant":[66],"throughout":[67],"training,":[68],"limiting":[69],"resilient":[70],"evolution.":[72],"In":[73],"work,":[75],"we":[76,138],"argue":[77],"that":[78,100,114,140,161,192],"\\textit{when}":[79],"signals":[81],"scheduled":[83],"can":[84],"be":[85],"as":[86,88],"important":[87],"\\textit{where}":[89],"they":[90],"allocated":[92],"across":[93,182],"tokens,":[94],"introduce":[96],"temporal":[98,156,174,193],"dimension":[99],"scheduling":[101,175,194],"criteria":[105],"over":[106],"course":[108],"RLVR":[110],"optimization.":[111],"We":[112],"find":[113],"prioritizing":[115],"targeted":[116],"emphasized":[118],"specific":[120],"behaviors,":[122,151,172],"gradually":[124],"attenuating":[125],"toward":[126],"general":[127,185],"leads":[129],"to":[130],"more":[131],"stable":[132],"efficient":[134],"dynamics.":[136,180],"Furthermore,":[137],"show":[139],"simple":[141],"trajectory":[142],"percentiles":[143],"provide":[144],"natural":[146],"perspective":[147],"distinguishing":[149],"effectively":[154],"scheduling.":[157],"Our":[158],"analysis":[159],"reveals":[160],"standard":[162],"substantially":[164],"sacrifices":[165],"entropy":[167],"when":[168],"simultaneously":[169],"accommodating":[170],"whereas":[173],"yields":[176],"healthier":[177],"evolution":[179],"Experiments":[181],"mathematical":[183],"reasoning":[186],"benchmarks":[187],"demonstrate":[188],"consistent":[189],"improvements,":[190],"suggesting":[191],"constitutes":[195],"promising":[197],"dimension.":[199]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}
