{"id":"https://openalex.org/W7160618840","doi":"https://doi.org/10.48550/arxiv.2605.05802","title":"Selective Rollout: Mid-Trajectory Termination for Multi-Sample Agent RL","display_name":"Selective Rollout: Mid-Trajectory Termination for Multi-Sample Agent RL","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160618840","doi":"https://doi.org/10.48550/arxiv.2605.05802"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.05802","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05802","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.2605.05802","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135713976","display_name":"Zhiyuan Zhai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhai, Zhiyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135713170","display_name":"Xin Wang","orcid":"https://orcid.org/0009-0001-6421-1611"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xin","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":0.4810999929904938,"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":0.4810999929904938,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.2110999971628189,"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/T11574","display_name":"Artificial Intelligence in Games","score":0.028699999675154686,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.522599995136261},{"id":"https://openalex.org/keywords/fraction","display_name":"Fraction (chemistry)","score":0.46970000863075256},{"id":"https://openalex.org/keywords/group","display_name":"Group (periodic table)","score":0.4345000088214874},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.3887999951839447},{"id":"https://openalex.org/keywords/randomness","display_name":"Randomness","score":0.3758000135421753},{"id":"https://openalex.org/keywords/ask-price","display_name":"Ask price","score":0.37459999322891235},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.36329999566078186},{"id":"https://openalex.org/keywords/tracing","display_name":"Tracing","score":0.30869999527931213}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6044999957084656},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.522599995136261},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.46970000863075256},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.4345000088214874},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41350001096725464},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3887999951839447},{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.3758000135421753},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.37459999322891235},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.36329999566078186},{"id":"https://openalex.org/C138673069","wikidata":"https://www.wikidata.org/wiki/Q322229","display_name":"Tracing","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2971999943256378},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.2809000015258789},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2791000008583069},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2687000036239624},{"id":"https://openalex.org/C124584101","wikidata":"https://www.wikidata.org/wiki/Q1053266","display_name":"Multiplier (economics)","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.260699987411499},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.05802","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05802","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.2605.05802","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05802","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Group-relative":[0],"RL":[1],"training":[2,13,48],"(GRPO)":[3],"samples":[4],"a":[5,31,54,176,186,191,207,210,255],"small":[6],"group":[7,62,153,170,192],"of":[8,53,87],"parallel":[9,159],"rollouts":[10,74,111,160],"for":[11,114],"every":[12,51],"prompt":[14,55,106],"and":[15,67,179,237],"uses":[16],"their":[17,110],"within-group":[18],"reward":[19,65],"spread":[20],"to":[21,174,254],"compute":[22],"per-trajectory":[23],"advantages.":[24],"In":[25],"agentic":[26],"environments":[27],"each":[28],"rollout":[29,52,118,131],"is":[30,94,171,263],"long":[32],"multi-turn":[33],"dialogue":[34],"with":[35,57,217,249],"one":[36],"LLM":[37],"call":[38],"per":[39],"step,":[40],"so":[41,71,90],"this":[42],"multi-sample":[43],"multiplier":[44],"dominates":[45],"the":[46,58,61,72,91,105,130,137,141,152,158,165,169,194,224,250],"total":[47],"cost.":[49],"When":[50],"ends":[56],"same":[59,166],"reward,":[60,178],"has":[63],"zero":[64],"variance":[66],"contributes":[68],"no":[69,76],"gradient,":[70],"extra":[73],"add":[75],"information;":[77],"such":[78,102],"groups":[79,103],"are":[80,112],"common":[81],"in":[82,230,258],"practice":[83],"(typically":[84],"around":[85],"40%":[86],"all":[88],"groups),":[89],"wasted-compute":[92],"fraction":[93],"substantial":[95],"rather":[96],"than":[97],"marginal.":[98],"Existing":[99],"methods":[100],"filter":[101],"at":[104,144,265],"level,":[107],"either":[108],"after":[109],"paid":[113],"or":[115],"before":[116],"any":[117],"begins,":[119],"but":[120],"both":[121],"decide":[122],"without":[123],"using":[124],"information":[125],"that":[126,151,189],"becomes":[127],"available":[128,264],"during":[129],"itself.":[132],"We":[133,184],"instead":[134],"ask":[135],"whether":[136],"in-group":[138],"divergence":[139],"between":[140,200],"partial":[142,202],"trajectories":[143],"an":[145],"intermediate":[146],"step":[147],"can":[148,181],"already":[149,162],"predict":[150],"will":[154],"be":[155],"zero-variance:":[156],"when":[157,193],"have":[161],"converged":[163],"on":[164,172,215,242],"action":[167,203],"prefix,":[168],"track":[173],"produce":[175],"single":[177],"we":[180],"stop":[182],"early.":[183],"propose":[185],"one-parameter":[187],"gate":[188],"stops":[190],"mean":[195],"pairwise":[196],"prefix":[197],"edit":[198],"distance":[199],"its":[201],"sequences":[204],"falls":[205],"below":[206],"threshold.":[208],"On":[209],"60-iteration":[211],"on-policy":[212],"GRPO":[213],"run":[214],"ALFWorld":[216],"Qwen2.5-7B,":[218],"averaged":[219],"over":[220],"four":[221],"random":[222],"seeds,":[223],"gated":[225],"arm":[226],"finishes":[227],"10.7%":[228],"faster":[229],"wall-clock":[231],"(bootstrap":[232],"95%":[233],"CI":[234],"excludes":[235],"0)":[236],"shifts":[238],"held-out":[239,251],"success":[240],"rate":[241],"50":[243],"unseen":[244],"tasks":[245],"by":[246],"+2.5":[247],"pp,":[248],"gain":[252],"tracing":[253],"measurable":[256],"reduction":[257],"zero-advantage":[259],"gradient-batch":[260],"dilution.":[261],"Code":[262],"https://github.com/zhiyuanZhai20/selective-rollout.":[266]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}
