{"id":"https://openalex.org/W7161697216","doi":"https://doi.org/10.48550/arxiv.2605.16345","title":"Goal-Conditioned Supervised Learning for LLM Fine-Tuning","display_name":"Goal-Conditioned Supervised Learning for LLM Fine-Tuning","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7161697216","doi":"https://doi.org/10.48550/arxiv.2605.16345"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.16345","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16345","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.2605.16345","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136468403","display_name":"Shijun Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Shijun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054681598","display_name":"Kaiwen Dong","orcid":"https://orcid.org/0000-0001-8244-9562"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Kaiwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136488977","display_name":"Xiang Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136476407","display_name":"Joydeep Ghosh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ghosh, Joydeep","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":0.3758000135421753,"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":0.3758000135421753,"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":0.12080000340938568,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.052400000393390656,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7027999758720398},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6162999868392944},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.5504000186920166},{"id":"https://openalex.org/keywords/offline-learning","display_name":"Offline learning","score":0.42980000376701355},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.39899998903274536},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3865000009536743},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.3702999949455261},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.3555000126361847},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.35089999437332153}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7519000172615051},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7027999758720398},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6708999872207642},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6646000146865845},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6162999868392944},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5504000186920166},{"id":"https://openalex.org/C2780490138","wikidata":"https://www.wikidata.org/wiki/Q7079636","display_name":"Offline learning","level":3,"score":0.42980000376701355},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3702999949455261},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.3555000126361847},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.35089999437332153},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.33629998564720154},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.320499986410141},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C2780102126","wikidata":"https://www.wikidata.org/wiki/Q10928179","display_name":"Online and offline","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.16345","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16345","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.2605.16345","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16345","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,43],"often":[3,94],"require":[4],"fine-tuning":[5,68,112,241],"to":[6,52,98,120,137,193,206],"better":[7,145,207],"align":[8],"their":[9],"behavior":[10],"with":[11],"user":[12],"intent":[13],"at":[14],"deployment.":[15],"Existing":[16],"approaches":[17,64],"are":[18,59],"commonly":[19],"divided":[20],"into":[21,82],"online":[22],"and":[23,44,50,70,129,185,212,228,248],"offline":[24,111,240],"paradigms.":[25],"Online":[26],"methods,":[27],"such":[28,65],"as":[29,66,109,125,159],"RL-based":[30],"alignment,":[31],"can":[32],"directly":[33,124],"optimize":[34],"outcome":[35],"quality":[36,166],"but":[37,62],"typically":[38,78],"rely":[39],"on":[40,88,221],"external":[41],"reward":[42],"iterative":[45],"rollouts,":[46],"making":[47],"them":[48],"costly":[49],"difficult":[51],"deploy":[53],"in":[54],"many":[55],"cases.":[56],"Offline":[57],"methods":[58],"more":[60],"efficient,":[61],"prevailing":[63],"supervised":[67,106,135,253],"(SFT)":[69],"direct":[71],"preference":[72,90],"optimization":[73],"(DPO)":[74],"remain":[75],"limited:":[76],"SFT":[77,184],"collapses":[79],"graded":[80,147],"feedback":[81,122],"binary":[83],"supervision,":[84],"while":[85,243],"DPO":[86],"depends":[87],"paired":[89],"data":[91,250],"that":[92,140,142,156,234],"is":[93,119],"unavailable":[95],"or":[96],"expensive":[97],"construct.":[99],"In":[100],"this":[101],"paper,":[102],"we":[103,149],"propose":[104,202],"goal-conditioned":[105],"learning":[107,158],"(GCSL)":[108],"an":[110,126],"framework":[113],"for":[114,230],"LLMs.":[115,216],"Our":[116],"core":[117],"idea":[118],"treat":[121],"signals":[123],"explicit":[127],"goal":[128,154,204],"train":[130],"the":[131,180,191,195,209,245],"model,":[132],"purely":[133],"through":[134],"learning,":[136],"generate":[138],"responses":[139],"achieve":[141],"goal.":[143],"To":[144],"exploit":[146],"feedback,":[148],"further":[150],"introduce":[151],"a":[152,164,173],"novel":[153],"formulation":[155],"defines":[157],"consistently":[160,237],"pursuing":[161],"outcomes":[162],"above":[163],"target":[165],"threshold,":[167],"rather":[168],"than":[169],"imitating":[170],"samples":[171],"from":[172],"selected":[174],"high-quality":[175],"subset.":[176],"This":[177],"design":[178],"mitigates":[179],"bounded-learning":[181],"effect":[182],"of":[183,198,215,252],"classic":[186],"GCSL":[187],"by":[188],"explicitly":[189],"guiding":[190],"model":[192],"learn":[194],"directional":[196],"progression":[197],"quality.":[199],"We":[200,217],"also":[201],"natural-language":[203],"representations":[205],"leverage":[208],"semantic":[210],"understanding":[211],"reasoning":[213],"capabilities":[214],"evaluate":[218],"our":[219,235],"method":[220],"three":[222],"tasks:":[223],"non-toxic":[224],"generation,":[225,227],"code":[226],"LLM":[229],"recommendation.":[231],"Results":[232],"show":[233],"approach":[236],"outperforms":[238],"standard":[239],"baselines":[242],"retaining":[244],"efficiency,":[246],"scalability,":[247],"simple":[249],"requirements":[251],"learning.":[254]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-20T00:00:00"}