{"id":"https://openalex.org/W7131099969","doi":"https://doi.org/10.48550/arxiv.2602.17931","title":"Memory-Based Advantage Shaping for LLM-Guided Reinforcement Learning","display_name":"Memory-Based Advantage Shaping for LLM-Guided Reinforcement Learning","publication_year":2026,"publication_date":"2026-02-20","ids":{"openalex":"https://openalex.org/W7131099969","doi":"https://doi.org/10.48550/arxiv.2602.17931"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.17931","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/A5093835444","display_name":"Narjes Nourzad","orcid":"https://orcid.org/0009-0008-6315-8282"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nourzad, Narjes","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125990149","display_name":"Carlee Joe-Wong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joe-Wong, Carlee","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5093835444"],"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":0.335999995470047,"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":0.335999995470047,"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":0.20640000700950623,"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/T10028","display_name":"Topic Modeling","score":0.07670000195503235,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.775600016117096},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6190000176429749},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6032000184059143},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5737000107765198},{"id":"https://openalex.org/keywords/sample-complexity","display_name":"Sample complexity","score":0.4973999857902527},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4648999869823456},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4259999990463257}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.775600016117096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.715399980545044},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6190000176429749},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6032000184059143},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5737000107765198},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5723999738693237},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5604000091552734},{"id":"https://openalex.org/C2778445095","wikidata":"https://www.wikidata.org/wiki/Q18354077","display_name":"Sample complexity","level":2,"score":0.4973999857902527},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4648999869823456},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4259999990463257},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4077000021934509},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.38999998569488525},{"id":"https://openalex.org/C2780490138","wikidata":"https://www.wikidata.org/wiki/Q7079636","display_name":"Offline learning","level":3,"score":0.36880001425743103},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.17931","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.2602.17931","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.17931","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.2602.17931","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"environments":[1,139],"with":[2,97,110,153],"sparse":[3],"or":[4],"delayed":[5],"rewards,":[6],"reinforcement":[7],"learning":[8,147],"(RL)":[9],"incurs":[10],"high":[11],"sample":[12,142],"complexity":[13],"due":[14],"to":[15,149,157],"the":[16,28,76,93,104,108,115],"large":[17,31],"number":[18],"of":[19,30],"interactions":[20],"needed":[21],"for":[22,35],"learning.":[23],"This":[24,101],"limitation":[25],"has":[26],"motivated":[27],"use":[29],"language":[32],"models":[33],"(LLMs)":[34],"subgoal":[36],"discovery":[37],"and":[38,55,69,75,124,144],"trajectory":[39],"guidance.":[40],"While":[41],"LLMs":[42],"can":[43],"support":[44],"exploration,":[45],"frequent":[46,161],"reliance":[47],"on":[48,121,131],"LLM":[49,73,133,162],"calls":[50],"raises":[51],"concerns":[52],"about":[53],"scalability":[54],"reliability.":[56],"We":[57],"address":[58],"these":[59],"challenges":[60],"by":[61],"constructing":[62],"a":[63,86],"memory":[64],"graph":[65],"that":[66,89,159],"encodes":[67],"subgoals":[68],"trajectories":[70,95],"from":[71],"both":[72],"guidance":[74,112],"agent's":[77,94],"own":[78],"successful":[79,99],"rollouts.":[80],"From":[81],"this":[82],"graph,":[83],"we":[84],"derive":[85],"utility":[87,102],"function":[88],"evaluates":[90],"how":[91],"closely":[92],"align":[96],"prior":[98],"strategies.":[100],"shapes":[103],"advantage":[105],"function,":[106],"providing":[107],"critic":[109],"additional":[111],"without":[113],"altering":[114],"reward.":[116],"Our":[117],"method":[118],"relies":[119],"primarily":[120],"offline":[122],"input":[123],"only":[125],"occasional":[126],"online":[127],"queries,":[128],"avoiding":[129],"dependence":[130],"continuous":[132],"supervision.":[134],"Preliminary":[135],"experiments":[136],"in":[137],"benchmark":[138],"show":[140],"improved":[141],"efficiency":[143],"faster":[145],"early":[146],"compared":[148],"baseline":[150],"RL":[151],"methods,":[152],"final":[154],"returns":[155],"comparable":[156],"methods":[158],"require":[160],"interaction.":[163]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-24T00:00:00"}
