{"id":"https://openalex.org/W7133939268","doi":"https://doi.org/10.48550/arxiv.2603.04022","title":"Rethinking the Efficiency and Effectiveness of Reinforcement Learning for Radiology Report Generation","display_name":"Rethinking the Efficiency and Effectiveness of Reinforcement Learning for Radiology Report Generation","publication_year":2026,"publication_date":"2026-03-04","ids":{"openalex":"https://openalex.org/W7133939268","doi":"https://doi.org/10.48550/arxiv.2603.04022"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.04022","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128142555","display_name":"Zilin Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lu, Zilin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128139245","display_name":"Ruifeng Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Ruifeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128157522","display_name":"Weiwei Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Weiwei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066605043","display_name":"Wanxing Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang, Wanxing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128162995","display_name":"Zhongyu Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Zhongyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109690974","display_name":"Sinuo Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Sinuo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128159965","display_name":"Yong Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Yong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128163118","display_name":"Ling Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ling","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128184719","display_name":"Jianpeng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jianpeng","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5128142555"],"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.42989999055862427,"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"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.42989999055862427,"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"}},{"id":"https://openalex.org/T11894","display_name":"Radiology practices and education","score":0.14920000731945038,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.09470000118017197,"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":0.7728999853134155},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6498000025749207},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4779999852180481},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.45249998569488525},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4429999887943268},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.33889999985694885},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3327000141143799},{"id":"https://openalex.org/keywords/clinical-practice","display_name":"Clinical Practice","score":0.3127000033855438}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7728999853134155},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6797000169754028},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6498000025749207},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.593500018119812},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5655999779701233},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4779999852180481},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.45249998569488525},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4429999887943268},{"id":"https://openalex.org/C19527891","wikidata":"https://www.wikidata.org/wiki/Q1120908","display_name":"Medical physics","level":1,"score":0.3682999908924103},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.33889999985694885},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3327000141143799},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3009999990463257},{"id":"https://openalex.org/C3018822202","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Patient data","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.29179999232292175},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C195910791","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Medical record","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C71405471","wikidata":"https://www.wikidata.org/wiki/Q757012","display_name":"Quality management","level":3,"score":0.2599000036716461},{"id":"https://openalex.org/C106436119","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assurance","level":3,"score":0.25519999861717224}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.04022","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.04022","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.04022","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":"pmh:doi:10.48550/arxiv.2603.04022","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"Radiologists":[0],"highly":[1],"desire":[2],"fully":[3],"automated":[4],"AI":[5],"for":[6,50,145],"radiology":[7,109],"report":[8],"generation":[9],"(R2G),":[10],"yet":[11],"existing":[12],"approaches":[13,161],"fall":[14],"short":[15],"in":[16,31,42,68,108,210],"clinical":[17,146],"utility.":[18],"Reinforcement":[19],"learning":[20],"(RL)":[21],"holds":[22],"potential":[23],"to":[24,181],"address":[25],"these":[26],"shortcomings,":[27],"but":[28],"its":[29],"adoption":[30],"this":[32,37,83],"task":[33],"remains":[34],"underexplored.":[35],"In":[36],"paper,":[38],"we":[39,54,85,101,135],"revisit":[40],"RL":[41,67,160,228],"terms":[43],"of":[44,58,66,106,120,127,173,221,226],"data":[45,59,73,90],"efficiency":[46],"and":[47,61,113,192],"optimization":[48],"effectiveness":[49],"R2G":[51],"tasks.":[52],"First,":[53],"explore":[55],"the":[56,64,104,117,125,155,170,189,227],"impact":[57],"quantity":[60],"quality":[62,74],"on":[63,188,213],"performance":[65,96,203],"medical":[69],"contexts,":[70],"revealing":[71],"that":[72,93,103,162,197],"plays":[75],"a":[76,87,150],"more":[77],"critical":[78,122],"role":[79],"than":[80],"quantity.":[81],"To":[82,132],"end,":[84],"propose":[86],"diagnostic":[88,151],"diversity-based":[89],"sampling":[91],"strategy":[92],"enables":[94],"comparable":[95],"with":[97],"fewer":[98,207],"samples.":[99,230],"Second,":[100],"observe":[102],"majority":[105],"tokens":[107,123,165,175],"reports":[110],"are":[111],"template-like":[112],"diagnostically":[114],"uninformative,":[115],"whereas":[116],"low":[118],"frequency":[119],"clinically":[121,183],"heightens":[124],"risk":[126],"being":[128],"overlooked":[129],"during":[130],"optimization.":[131],"tackle":[133],"this,":[134],"introduce":[136],"Diagnostic":[137],"Token-weighted":[138],"Policy":[139],"Optimization":[140],"(DiTPO),":[141],"which":[142],"directly":[143],"optimizes":[144],"accuracy":[147],"by":[148],"using":[149,223],"F1":[152,219],"score":[153,220],"as":[154],"reward":[156],"signal.":[157],"Unlike":[158],"standard":[159],"treat":[163],"all":[164],"equally,":[166],"DiTPO":[167],"explicitly":[168],"models":[169],"varying":[171],"importance":[172],"different":[174],"through":[176],"rule-":[177],"or":[178],"gradient-based":[179],"mechanisms":[180],"prioritize":[182],"relevant":[184],"content.":[185],"Extensive":[186],"experiments":[187],"MIMIC-CXR,":[190,214],"IU-Xray,":[191],"CheXpert":[193],"Plus":[194],"datasets":[195],"demonstrate":[196],"our":[198,215],"framework":[199,216],"achieves":[200],"state-of-the-art":[201],"(SOTA)":[202],"while":[204],"requiring":[205],"substantially":[206],"training":[208,229],"samples":[209],"RL.":[211],"Notably,":[212],"attains":[217],"an":[218],"0.516":[222],"only":[224],"20%":[225]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-03-06T00:00:00"}
