{"id":"https://openalex.org/W7164513051","doi":"https://doi.org/10.48550/arxiv.2606.13680","title":"Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning","display_name":"Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning","publication_year":2026,"publication_date":"2026-06-11","ids":{"openalex":"https://openalex.org/W7164513051","doi":"https://doi.org/10.48550/arxiv.2606.13680"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.13680","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13680","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.2606.13680","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091852012","display_name":"Zilin Xiao","orcid":"https://orcid.org/0000-0001-8686-718X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Zilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109597339","display_name":"Q L","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Qi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061989398","display_name":"Chun-cheng Jason Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Chun-cheng Jason","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025922566","display_name":"Xintao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xintao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078548229","display_name":"Avinash Atreya","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Atreya, Avinash","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138518774","display_name":"Hanjie Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Hanjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5027328044","display_name":"Vicente Ord\u00f3\u00f1ez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ordonez, Vicente","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.4025000035762787,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.4025000035762787,"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":0.30320000648498535,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.03480000048875809,"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":0.7330999970436096},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6330999732017517},{"id":"https://openalex.org/keywords/commonsense-reasoning","display_name":"Commonsense reasoning","score":0.5022000074386597},{"id":"https://openalex.org/keywords/analogy","display_name":"Analogy","score":0.43220001459121704},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.40880000591278076},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4059999883174896},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.4007999897003174},{"id":"https://openalex.org/keywords/case-based-reasoning","display_name":"Case-based reasoning","score":0.3582000136375427}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7330999970436096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6947000026702881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6590999960899353},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6330999732017517},{"id":"https://openalex.org/C193221554","wikidata":"https://www.wikidata.org/wiki/Q5153664","display_name":"Commonsense reasoning","level":2,"score":0.5022000074386597},{"id":"https://openalex.org/C521332185","wikidata":"https://www.wikidata.org/wiki/Q185816","display_name":"Analogy","level":2,"score":0.43220001459121704},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.40880000591278076},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4059999883174896},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.4007999897003174},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3718000054359436},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3659999966621399},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.3582000136375427},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C85847156","wikidata":"https://www.wikidata.org/wiki/Q59015987","display_name":"Verifiable secret sharing","level":3,"score":0.3133000135421753},{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.13680","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13680","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.2606.13680","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13680","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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6065658926963806}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Retrieval-augmented":[0],"generation":[1],"(RAG)":[2],"has":[3],"become":[4],"a":[5,31,43,60,77,179],"standard":[6,149],"mechanism":[7],"for":[8,27,138,168],"grounding":[9],"language":[10,65],"models":[11,66],"in":[12,188],"external":[13],"knowledge,":[14],"yet":[15],"conventional":[16],"retrieval":[17,128,177],"based":[18],"on":[19],"lexical":[20],"or":[21,191],"semantic":[22,88],"similarity":[23],"is":[24,178],"poorly":[25],"suited":[26],"complex":[28],"reasoning":[29,52,84,110,136,144],"tasks:":[30],"semantically":[32],"similar":[33],"problem":[34,46],"may":[35,47],"demand":[36],"an":[37],"entirely":[38],"different":[39,45],"solution":[40,131],"strategy,":[41],"while":[42],"superficially":[44],"share":[48],"the":[49,93,105,119],"same":[50],"underlying":[51],"pattern.":[53],"We":[54,116],"propose":[55],"Retrieval-Augmented":[56],"Reinforcement":[57],"Fine-Tuning":[58],"(RA-RFT),":[59],"post-training":[61],"framework":[62],"that":[63,79,126,133,175],"teaches":[64],"to":[67,75,108,186],"reason":[68],"by":[69,82,161],"analogy.":[70],"RA-RFT":[71,146],"uses":[72],"gold-relevance":[73],"distillation":[74],"train":[76],"retriever":[78],"ranks":[80],"contexts":[81,123],"expected":[83],"benefit":[85],"rather":[86],"than":[87],"overlap,":[89],"and":[90,124,163,170,184],"then":[91],"fine-tunes":[92],"policy":[94],"model":[95,106],"via":[96],"reinforcement":[97,150],"fine-tuning":[98,151],"methods":[99],"with":[100],"retrieved":[101,122],"analogous":[102],"demonstrations,":[103],"so":[104],"learns":[107],"leverage":[109],"traces":[111],"under":[112],"verifiable":[113],"outcome":[114],"rewards.":[115],"further":[117],"analyze":[118],"diversity":[120],"of":[121,182],"find":[125],"reasoning-aware":[127,176],"surfaces":[129],"complementary":[130,180],"strategies":[132],"provide":[134],"distinct":[135],"scaffolds":[137],"individual":[139],"problems.":[140],"Across":[141],"challenging":[142],"mathematical":[143],"benchmarks,":[145],"consistently":[147],"outperforms":[148],"methods.":[152],"For":[153],"example,":[154],"it":[155],"improves":[156],"AIME":[157],"2025":[158],"average@32":[159],"accuracy":[160],"7.1":[162],"2.8":[164],"points":[165],"over":[166],"GRPO":[167],"Qwen3-1.7B":[169],"Qwen3-4B":[171],"respectively":[172],"--":[173],"suggesting":[174],"axis":[181],"improvement":[183],"orthogonal":[185],"advances":[187],"reward":[189],"design":[190],"training":[192],"curricula.":[193]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-13T00:00:00"}