{"id":"https://openalex.org/W7162660453","doi":"https://doi.org/10.48550/arxiv.2605.28421","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","display_name":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","publication_year":2026,"publication_date":"2026-05-27","ids":{"openalex":"https://openalex.org/W7162660453","doi":"https://doi.org/10.48550/arxiv.2605.28421"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.28421","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28421","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":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.2605.28421","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109741356","display_name":"Caijun Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Caijun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137300140","display_name":"Changyi Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Changyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137201304","display_name":"Zhongyuan Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Zhongyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137303811","display_name":"Yixin Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Yixin","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":0.33230000734329224,"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":0.33230000734329224,"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":0.12129999697208405,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.059700001031160355,"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":0.6796000003814697},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6780999898910522},{"id":"https://openalex.org/keywords/bootstrapping","display_name":"Bootstrapping (finance)","score":0.6108999848365784},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4959000051021576},{"id":"https://openalex.org/keywords/prefix","display_name":"Prefix","score":0.4269999861717224},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.41130000352859497}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7825000286102295},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6796000003814697},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6780999898910522},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.6108999848365784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5895000100135803},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4959000051021576},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4853000044822693},{"id":"https://openalex.org/C141603448","wikidata":"https://www.wikidata.org/wiki/Q134830","display_name":"Prefix","level":2,"score":0.4269999861717224},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.39570000767707825},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.3743000030517578},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2529999911785126},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.2515999972820282},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.28421","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28421","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.28421","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28421","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":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":{"Reinforcement":[0],"learning":[1,41,96],"has":[2],"become":[3],"a":[4,39,91,106],"central":[5],"paradigm":[6],"for":[7,77,120,159],"advancing":[8],"reasoning":[9,70,110,141,161],"in":[10,162],"large":[11,163],"language":[12,164],"models,":[13],"yet":[14],"most":[15],"existing":[16],"methods":[17],"still":[18],"depend":[19],"on":[20,58,86],"stronger":[21,59,125,145],"teacher":[22,126],"models":[23],"or":[24,61,124],"heavily":[25],"curated":[26],"difficult":[27],"datasets,":[28],"limiting":[29],"scalable":[30,82,156],"capability":[31],"improvement.":[32],"In":[33],"this":[34],"paper,":[35],"we":[36],"introduce":[37],"DenoiseRL,":[38],"reinforcement":[40],"framework":[42],"that":[43],"substitutes":[44],"external":[45,87],"supervision":[46,60],"with":[47],"recovery-oriented":[48],"optimization":[49],"over":[50],"failures":[51],"from":[52,68,101],"weak":[53],"models.":[54,127,165],"Instead":[55],"of":[56],"relying":[57],"carefully":[62],"engineered":[63],"data,":[64],"DenoiseRL":[65,108,129],"learns":[66],"directly":[67],"incorrect":[69],"traces":[71],"by":[72],"converting":[73],"them":[74],"into":[75],"opportunities":[76],"improvement,":[78],"making":[79],"training":[80,114,149],"more":[81,94],"and":[83,93,112,139,143,155],"less":[84],"dependent":[85],"resources.":[88],"This":[89],"yields":[90],"richer":[92],"diverse":[95],"signal,":[97],"improving":[98,160],"exploration":[99],"efficiency":[100,115],"imperfect":[102],"model":[103],"behavior.":[104],"As":[105],"result,":[107],"improves":[109],"performance":[111],"overall":[113],"while":[116],"reducing":[117],"the":[118],"need":[119],"expensive":[121],"data":[122],"curation":[123],"Empirically,":[128],"consistently":[130],"outperforms":[131],"strong":[132],"on-policy":[133],"RL":[134],"baselines":[135],"across":[136],"competitive":[137],"mathematical":[138],"general":[140],"benchmarks":[142],"promotes":[144],"self-corrective":[146],"behavior":[147],"as":[148],"difficulty":[150],"increases,":[151],"highlighting":[152],"an":[153],"effective":[154],"alternative":[157],"pathway":[158]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-29T00:00:00"}
