{"id":"https://openalex.org/W7133348081","doi":"https://doi.org/10.48550/arxiv.2603.00454","title":"Rooted Absorbed Prefix Trajectory Balance with Submodular Replay for GFlowNet Training","display_name":"Rooted Absorbed Prefix Trajectory Balance with Submodular Replay for GFlowNet Training","publication_year":2026,"publication_date":"2026-02-28","ids":{"openalex":"https://openalex.org/W7133348081","doi":"https://doi.org/10.48550/arxiv.2603.00454"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.00454","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00454","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.2603.00454","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127965783","display_name":"Xi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009740454","display_name":"Wenbo Lu","orcid":"https://orcid.org/0000-0003-4977-7665"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Wenbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127930985","display_name":"Shengjie Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shengjie","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":0.4097000062465668,"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":0.4097000062465668,"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":0.12919999659061432,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.06040000170469284,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/prefix","display_name":"Prefix","score":0.82669997215271},{"id":"https://openalex.org/keywords/submodular-set-function","display_name":"Submodular set function","score":0.7788000106811523},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5591999888420105},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4327000081539154},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.3910999894142151},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.3903000056743622},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.362199991941452}],"concepts":[{"id":"https://openalex.org/C141603448","wikidata":"https://www.wikidata.org/wiki/Q134830","display_name":"Prefix","level":2,"score":0.82669997215271},{"id":"https://openalex.org/C178621042","wikidata":"https://www.wikidata.org/wiki/Q7631710","display_name":"Submodular set function","level":2,"score":0.7788000106811523},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6898999810218811},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5591999888420105},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4327000081539154},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40059998631477356},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3910999894142151},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3903000056743622},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3837999999523163},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.37310001254081726},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.362199991941452},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.3483999967575073},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C168031717","wikidata":"https://www.wikidata.org/wiki/Q1530280","display_name":"Balance (ability)","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.30570000410079956},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3034000098705292},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29499998688697815},{"id":"https://openalex.org/C171078966","wikidata":"https://www.wikidata.org/wiki/Q111029","display_name":"Root (linguistics)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C2779136372","wikidata":"https://www.wikidata.org/wiki/Q10283002","display_name":"Information flow","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C2779664074","wikidata":"https://www.wikidata.org/wiki/Q3518405","display_name":"Terminal (telecommunication)","level":2,"score":0.26010000705718994}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.00454","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00454","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.2603.00454","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.00454","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generative":[0],"Flow":[1],"Networks":[2],"(GFlowNets)":[3],"enable":[4],"fine-tuning":[5],"large":[6],"language":[7],"models":[8],"to":[9,17,30,37,73],"approximate":[10],"reward-proportional":[11],"posteriors,":[12],"but":[13],"they":[14],"remain":[15],"prone":[16],"mode":[18],"collapse,":[19],"manifesting":[20],"as":[21,110],"prefix":[22,56],"collapse":[23],"and":[24,40,69,104,126],"length":[25],"bias.":[26],"We":[27,52],"attribute":[28],"this":[29],"two":[31],"factors:":[32],"(i)":[33],"weak":[34],"credit":[35],"assignment":[36],"early":[38],"prefixes,":[39],"(ii)":[41],"biased":[42],"replay":[43,96],"that":[44,62,99],"induces":[45],"a":[46,94],"shifted,":[47],"non-representative":[48],"training":[49],"flow":[50],"distribution.":[51],"propose":[53],"Rooted":[54],"absorbed":[55,77],"Trajectory":[57],"Balance":[58],"RapTB,":[59],"an":[60],"objective":[61],"anchors":[63],"subtrajectory":[64],"supervision":[65],"at":[66],"the":[67],"root":[68],"propagates":[70],"terminal":[71],"rewards":[72],"intermediate":[74],"prefixes":[75],"via":[76],"suffix-based":[78],"backups,":[79],"providing":[80],"dense":[81],"prefix-level":[82],"learning":[83],"signals.":[84],"To":[85],"mitigate":[86],"replay-induced":[87],"distribution":[88],"shift,":[89],"we":[90],"further":[91],"introduce":[92],"SubM,":[93],"submodular":[95],"refresh":[97],"strategy":[98],"promotes":[100],"both":[101],"high":[102,131],"reward":[103],"diversity.":[105],"Empirically,":[106],"on":[107],"tasks":[108],"such":[109],"molecule":[111],"generation":[112],"with":[113,120],"LLM":[114],"using":[115],"SMILES":[116],"strings,":[117],"RapTB":[118],"combined":[119],"SubM":[121],"consistently":[122],"improves":[123],"optimization":[124],"performance":[125],"molecular":[127],"diversity":[128],"while":[129],"preserving":[130],"validity.":[132]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-04T00:00:00"}
