{"id":"https://openalex.org/W7168179300","doi":"https://doi.org/10.48550/arxiv.2607.09042","title":"Learning More from Less: Reinforcement Learning from Hindsight","display_name":"Learning More from Less: Reinforcement Learning from Hindsight","publication_year":2026,"publication_date":"2026-07-10","ids":{"openalex":"https://openalex.org/W7168179300","doi":"https://doi.org/10.48550/arxiv.2607.09042"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.09042","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.09042","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.09042","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121651138","display_name":"Iris Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Iris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027856457","display_name":"S. S. Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Sunshine","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120491247","display_name":"John Marangola","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marangola, John","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044299514","display_name":"Nitish Dashora","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dashora, Nitish","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052597908","display_name":"R K Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Richard","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107850087","display_name":"Thomas Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Thomas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069088584","display_name":"Zexue He","orcid":"https://orcid.org/0009-0001-9733-0545"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Zexue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008866420","display_name":"Yuheng Zhi","orcid":"https://orcid.org/0000-0003-4714-3028"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhi, Yuheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140525192","display_name":"Alex Pentland","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pentland, Alex","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140540557","display_name":"Pulkit Agrawal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agrawal, Pulkit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140526809","display_name":"Zhang-Wei Hong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hong, Zhang-Wei","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":0.850600004196167,"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":0.850600004196167,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.051899999380111694,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.028599999845027924,"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/hindsight-bias","display_name":"Hindsight bias","score":0.9790999889373779},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7333999872207642},{"id":"https://openalex.org/keywords/sample-complexity","display_name":"Sample complexity","score":0.5849999785423279},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5830000042915344},{"id":"https://openalex.org/keywords/policy-learning","display_name":"Policy learning","score":0.43849998712539673},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.3328000009059906},{"id":"https://openalex.org/keywords/statistical-learning","display_name":"Statistical learning","score":0.3296999931335449}],"concepts":[{"id":"https://openalex.org/C10347200","wikidata":"https://www.wikidata.org/wiki/Q1960297","display_name":"Hindsight bias","level":2,"score":0.9790999889373779},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7333999872207642},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6827999949455261},{"id":"https://openalex.org/C2778445095","wikidata":"https://www.wikidata.org/wiki/Q18354077","display_name":"Sample complexity","level":2,"score":0.5849999785423279},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5830000042915344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4593000113964081},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33090001344680786},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.29100000858306885},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.2669000029563904},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.09042","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.09042","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.09042","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.09042","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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],"(RL)":[2],"is":[3,65],"increasingly":[4],"used":[5],"to":[6,22,51,82],"post-train":[7],"vision-language-action":[8],"(VLA)":[9],"models,":[10],"but":[11],"every":[12,43],"update":[13],"consumes":[14],"robot":[15],"rollouts":[16,90,117,134],"that":[17],"are":[18],"slow":[19],"and":[20,48,105,118,125,132,170,182],"costly":[21],"collect,":[23],"making":[24],"sample":[25,168],"efficiency":[26],"a":[27,38,62,66,69,109,113,172,184],"central":[28],"concern.":[29],"Manipulation":[30],"tasks":[31,93],"typically":[32],"provide":[33],"only":[34,161],"sparse":[35],"rewards,":[36],"so":[37],"weak":[39],"policy":[40,127,146],"fails":[41],"almost":[42],"rollout":[44],"early":[45],"in":[46,142,167],"training":[47],"has":[49],"little":[50],"learn":[52,147],"from,":[53],"even":[54],"when":[55],"those":[56],"failures":[57],"execute":[58],"coherent":[59],"behavior.":[60],"Such":[61],"failure,":[63],"however,":[64],"success":[67],"at":[68],"different":[70],"task.":[71],"We":[72],"present":[73],"Learning":[74],"from":[75,149],"Hindsight":[76],"(LfH),":[77],"which":[78],"brings":[79],"hindsight":[80,110],"relabeling":[81,141],"RL":[83,159],"post-training":[84],"of":[85,115],"VLAs":[86,137],"by":[87],"scoring":[88,119],"failed":[89,116],"against":[91],"the":[92,103,106,126,130,145,150],"they":[94],"actually":[95],"achieved.":[96],"A":[97],"single":[98],"vision-language":[99],"model":[100],"relabels":[101],"both":[102],"instruction":[104,111],"reward,":[107],"proposing":[108],"for":[112],"group":[114],"how":[120],"well":[121],"each":[122],"satisfies":[123],"it,":[124],"trains":[128],"on":[129,183],"relabeled":[131],"original":[133],"jointly.":[135],"Because":[136],"generalize":[138],"across":[139,179],"language,":[140],"language":[143],"lets":[144],"more":[148],"same":[151],"trajectories.":[152],"On":[153],"out-of-distribution":[154],"LIBERO-PRO":[155],"tasks,":[156],"where":[157],"standard":[158],"improves":[160],"slowly,":[162],"LfH":[163],"achieves":[164],"$5\\times$":[165],"improvement":[166],"efficiency,":[169],"outperforms":[171],"dense":[173],"progress-reward":[174],"baseline.":[175],"The":[176],"gains":[177],"hold":[178],"VLA":[180],"backbones":[181],"physical":[185],"Franka":[186],"robot.":[187]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-14T00:00:00"}