{"id":"https://openalex.org/W7129205639","doi":"https://doi.org/10.48550/arxiv.2602.14169","title":"Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling","display_name":"Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling","publication_year":2026,"publication_date":"2026-02-15","ids":{"openalex":"https://openalex.org/W7129205639","doi":"https://doi.org/10.48550/arxiv.2602.14169"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.14169","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126182159","display_name":"Yiran Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Yiran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126201947","display_name":"Zhongjian Qiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiao, Zhongjian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126220056","display_name":"Yingqi Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Yingqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126180241","display_name":"Jie Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124954745","display_name":"Dan Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Dan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126177900","display_name":"Ruiqing Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ruiqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126214919","display_name":"Shuang Qiu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Shuang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126209191","display_name":"Lijie Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Lijie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"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":0.3124000132083893,"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":0.3124000132083893,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.18119999766349792,"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":0.06639999896287918,"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":0.7174000144004822},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6470999717712402},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.6370999813079834},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5446000099182129},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4519999921321869},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4465999901294708}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7383999824523926},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7174000144004822},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6754000186920166},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6470999717712402},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.6370999813079834},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5446000099182129},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5257999897003174},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4519999921321869},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4465999901294708},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.4334999918937683},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.4115000069141388},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2578999996185303},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.14169","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.14169","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.14169","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":"pmh:doi:10.48550/arxiv.2602.14169","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"Effective":[0],"exploration":[1,85],"is":[2,170],"a":[3,17,81,104,138],"key":[4,101],"challenge":[5],"in":[6],"reinforcement":[7],"learning":[8,146],"for":[9],"large":[10],"language":[11,25],"models:":[12],"discovering":[13,132],"high-quality":[14],"trajectories":[15,41],"within":[16,90],"limited":[18],"sampling":[19,59],"budget":[20],"from":[21,36,147],"the":[22,37,129],"vast":[23],"natural":[24],"sequence":[26],"space.":[27],"Existing":[28],"methods":[29,49],"face":[30],"notable":[31],"limitations:":[32],"GRPO":[33],"samples":[34],"exclusively":[35],"root,":[38],"saturating":[39],"high-probability":[40],"while":[42],"leaving":[43],"deep,":[44],"error-prone":[45],"states":[46,89],"under-explored.":[47],"Tree-based":[48],"blindly":[50],"disperse":[51],"budgets":[52],"across":[53],"trivial":[54],"or":[55],"unrecoverable":[56],"states,":[57],"causing":[58],"dilution":[60],"that":[61,83,109,142,157],"fails":[62],"to":[63,116,127],"uncover":[64],"rare":[65],"correct":[66,133],"suffixes":[67],"and":[68,113,136,165],"destabilizes":[69],"local":[70,121,148],"baselines.":[71,168],"To":[72],"address":[73],"this,":[74],"we":[75],"propose":[76],"Deep":[77],"Dense":[78],"Exploration":[79],"(DDE),":[80],"strategy":[82],"focuses":[84],"on":[86,152],"$\\textit{pivots}$-deep,":[87],"recoverable":[88],"unsuccessful":[91],"trajectories.":[92],"We":[93],"instantiate":[94],"DDE":[95],"with":[96],"DEEP-GRPO,":[97],"which":[98],"introduces":[99],"three":[100],"innovations:":[102],"(1)":[103],"lightweight":[105],"data-driven":[106],"utility":[107],"function":[108],"automatically":[110],"balances":[111],"recoverability":[112],"depth":[114],"bias":[115],"identify":[117],"pivot":[118,126],"states;":[119],"(2)":[120],"dense":[122],"resampling":[123],"at":[124,172],"each":[125],"increase":[128],"probability":[130],"of":[131],"subsequent":[134],"trajectories;":[135],"(3)":[137],"dual-stream":[139],"optimization":[140],"objective":[141],"decouples":[143],"global":[144],"policy":[145],"corrective":[149],"updates.":[150],"Experiments":[151],"mathematical":[153],"reasoning":[154],"benchmarks":[155],"demonstrate":[156],"our":[158],"method":[159],"consistently":[160],"outperforms":[161],"GRPO,":[162],"tree-based":[163],"methods,":[164],"other":[166],"strong":[167],"Code":[169],"available":[171],"https://github.com/AgentCombo/DEEP-GRPO":[173]},"counts_by_year":[],"updated_date":"2026-06-16T07:32:37.131356","created_date":"2026-02-18T00:00:00"}