{"id":"https://openalex.org/W7123646753","doi":"https://doi.org/10.48550/arxiv.2601.07389","title":"On the Non-decoupling of Supervised Fine-tuning and Reinforcement Learning in Post-training","display_name":"On the Non-decoupling of Supervised Fine-tuning and Reinforcement Learning in Post-training","publication_year":2026,"publication_date":"2026-01-12","ids":{"openalex":"https://openalex.org/W7123646753","doi":"https://doi.org/10.48550/arxiv.2601.07389"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.07389","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.07389","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.07389","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047520993","display_name":"Xueyan Niu","orcid":"https://orcid.org/0000-0001-5713-1739"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Niu, Xueyan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109735389","display_name":"Bo Bai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bai, Bo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122987478","display_name":"Wei Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101915841","display_name":"Weixi Zhang","orcid":"https://orcid.org/0009-0007-4769-2779"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Weixi","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5047520993"],"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":0.1703999936580658,"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":0.1703999936580658,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.14139999449253082,"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/T10028","display_name":"Topic Modeling","score":0.08609999716281891,"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":0.8080000281333923},{"id":"https://openalex.org/keywords/decoupling","display_name":"Decoupling (probability)","score":0.5375000238418579},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.38989999890327454},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.3822999894618988},{"id":"https://openalex.org/keywords/temporal-difference-learning","display_name":"Temporal difference learning","score":0.265500009059906}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8080000281333923},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.629800021648407},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5504000186920166},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.5375000238418579},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3822999894618988},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3027999997138977},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.265500009059906},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23149999976158142},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2298000007867813}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.07389","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.07389","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.07389","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.07389","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":"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":{"Post-training":[0],"of":[1,51,63,116],"large":[2],"language":[3],"models":[4,45],"routinely":[5],"interleaves":[6],"supervised":[7],"fine-tuning":[8],"(SFT)":[9],"with":[10],"reinforcement":[11],"learning":[12],"(RL).":[13],"These":[14],"two":[15],"methods":[16],"have":[17,46],"different":[18],"objectives:":[19],"SFT":[20,53,83,86,92,108],"minimizes":[21],"the":[22,49,94,103,120],"cross-entropy":[23],"loss":[24,84,115],"between":[25],"model":[26],"outputs":[27],"and":[28,54,88,109],"expert":[29],"responses,":[30],"while":[31],"RL":[32,55,81,110],"maximizes":[33],"reward":[34,95],"signals":[35],"derived":[36],"from":[37],"human":[38],"preferences":[39],"or":[40],"rule-based":[41],"verifiers.":[42],"Modern":[43],"reasoning":[44],"widely":[47],"adopted":[48],"practice":[50],"alternating":[52],"training.":[56],"However,":[57],"there":[58],"is":[59,73],"no":[60],"theoretical":[61],"account":[62],"whether":[64],"they":[65],"can":[66],"be":[67,112],"decoupled.":[68],"We":[69],"prove":[70],"that":[71,107],"decoupling":[72],"impossible":[74],"in":[75,119],"either":[76],"order:":[77],"(1)":[78],"SFT-then-RL":[79],"coupling:":[80,91],"increases":[82],"under":[85],"optimality":[87],"(2)":[89],"RL-then-SFT":[90],"lowers":[93],"achieved":[96],"by":[97],"RL.":[98],"Experiments":[99],"on":[100],"Qwen3-0.6B":[101],"confirm":[102],"predicted":[104],"degradation,":[105],"verifying":[106],"cannot":[111],"separated":[113],"without":[114],"prior":[117],"performance":[118],"post-training":[121]},"counts_by_year":[],"updated_date":"2026-01-14T23:44:37.837170","created_date":"2026-01-14T00:00:00"}
