{"id":"https://openalex.org/W7135240098","doi":"https://doi.org/10.48550/arxiv.2603.11193","title":"DeReason: A Difficulty-Aware Curriculum Improves Decoupled SFT-then-RL Training for General Reasoning","display_name":"DeReason: A Difficulty-Aware Curriculum Improves Decoupled SFT-then-RL Training for General Reasoning","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135240098","doi":"https://doi.org/10.48550/arxiv.2603.11193"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.11193","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11193","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.2603.11193","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045240612","display_name":"Hanxu Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Hanxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128979649","display_name":"Yuxuan (Melody) Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yuxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128987445","display_name":"Maggie Huan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huan, Maggie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129093133","display_name":"Jannis Vamvas","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vamvas, Jannis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129036074","display_name":"Yinya Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Yinya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128962759","display_name":"Zhijiang Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Zhijiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128938767","display_name":"Rico Sennrich","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sennrich, Rico","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"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":0.2531000077724457,"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":0.2531000077724457,"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":0.14550000429153442,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.06880000233650208,"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":0.6355000138282776},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4309000074863434},{"id":"https://openalex.org/keywords/general-purpose","display_name":"General purpose","score":0.4259999990463257},{"id":"https://openalex.org/keywords/curriculum","display_name":"Curriculum","score":0.4203999936580658},{"id":"https://openalex.org/keywords/decoupling","display_name":"Decoupling (probability)","score":0.4088999927043915},{"id":"https://openalex.org/keywords/verifiable-secret-sharing","display_name":"Verifiable secret sharing","score":0.32690000534057617},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.31630000472068787}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6355000138282776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.619700014591217},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.565500020980835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46209999918937683},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4309000074863434},{"id":"https://openalex.org/C2982832238","wikidata":"https://www.wikidata.org/wiki/Q5531640","display_name":"General purpose","level":2,"score":0.4259999990463257},{"id":"https://openalex.org/C47177190","wikidata":"https://www.wikidata.org/wiki/Q207137","display_name":"Curriculum","level":2,"score":0.4203999936580658},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.4088999927043915},{"id":"https://openalex.org/C85847156","wikidata":"https://www.wikidata.org/wiki/Q59015987","display_name":"Verifiable secret sharing","level":3,"score":0.32690000534057617},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C2780264999","wikidata":"https://www.wikidata.org/wiki/Q7445032","display_name":"Security domain","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.28690001368522644},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.26759999990463257},{"id":"https://openalex.org/C105002631","wikidata":"https://www.wikidata.org/wiki/Q4833645","display_name":"Subject-matter expert","level":3,"score":0.2612999975681305},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.11193","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11193","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.2603.11193","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11193","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":[{"score":0.8027462959289551,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"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],"with":[2],"Verifiable":[3],"Rewards":[4],"(RLVR)":[5],"has":[6],"emerged":[7],"as":[8],"a":[9,60,119,157,212,226],"powerful":[10],"paradigm":[11,31],"for":[12,63,124,163,183,222],"eliciting":[13],"reasoning":[14,132],"capabilities":[15],"in":[16,21,47],"large":[17],"language":[18],"models,":[19],"particularly":[20],"mathematics":[22],"and":[23,45,76,105,140,155,186,193,206,220,229],"coding.":[24],"While":[25],"recent":[26],"efforts":[27],"have":[28],"extended":[29],"this":[30,53,172],"to":[32,70,148,150,165],"broader":[33],"general":[34,64,125,191,223],"scientific":[35],"(STEM)":[36],"domains,":[37,66],"the":[38,99,181,216],"complex":[39,167],"interplay":[40,217],"between":[41,112,218],"supervised":[42,81],"fine-tuning":[43,82],"(SFT)":[44,83],"RL":[46,67,92,164,221],"these":[48],"contexts":[49],"remains":[50],"underexplored.":[51],"In":[52],"paper,":[54],"we":[55,116],"conduct":[56],"controlled":[57],"experiments":[58,189],"revealing":[59],"critical":[61],"challenge:":[62],"STEM":[65,192],"applied":[68],"directly":[69],"base":[71],"models":[72],"is":[73,77,110],"highly":[74,227],"sample-inefficient":[75],"consistently":[78],"surpassed":[79],"by":[80,91,131],"on":[84,190],"moderate-quality":[85],"responses.":[86],"Yet":[87],"sequential":[88,184],"SFT":[89,149,185,219],"followed":[90],"can":[93],"further":[94],"improve":[95],"performance,":[96],"suggesting":[97],"that":[98,106,171,197],"two":[100],"stages":[101],"play":[102],"complementary":[103],"roles,":[104],"how":[107],"training":[108,129,201],"data":[109,121,130,182],"allocated":[111],"them":[113],"matters.":[114],"Therefore,":[115],"propose":[117],"DeReason,":[118],"difficulty-based":[120],"decoupling":[122,174],"strategy":[123],"reasoning.":[126,168],"DeReason":[127],"partitions":[128],"intensity":[133],"estimated":[134],"via":[135],"LLM-based":[136],"scoring":[137],"into":[138],"reasoning-intensive":[139],"non-reasoning-intensive":[141,146],"subsets.":[142],"It":[143],"allocates":[144],"broad-coverage,":[145],"problems":[147,162],"establish":[151],"foundational":[152],"domain":[153],"knowledge,":[154],"reserves":[156],"focused":[158],"subset":[159],"of":[160,215],"difficult":[161],"cultivate":[166],"We":[169],"demonstrate":[170,196],"principled":[173],"yields":[175],"better":[176],"performance":[177],"than":[178],"randomly":[179],"splitting":[180],"RL.":[187],"Extensive":[188],"mathematical":[194],"benchmarks":[195],"our":[198],"decoupled":[199],"curriculum":[200],"significantly":[202],"outperforms":[203],"SFT-only,":[204],"RL-only,":[205],"random-split":[207],"baselines.":[208],"Our":[209],"work":[210],"provides":[211],"systematic":[213],"study":[214],"reasoning,":[224],"offering":[225],"effective":[228],"generalized":[230],"post-training":[231],"recipe.":[232]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-14T00:00:00"}
