{"id":"https://openalex.org/W7160847285","doi":"https://doi.org/10.48550/arxiv.2605.07394","title":"BalCapRL: A Balanced Framework for RL-Based MLLM Image Captioning","display_name":"BalCapRL: A Balanced Framework for RL-Based MLLM Image Captioning","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160847285","doi":"https://doi.org/10.48550/arxiv.2605.07394"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07394","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07394","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.07394","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135855059","display_name":"Shaokai Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Shaokai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114452727","display_name":"Vasileios Saveris","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saveris, Vasileios","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007342270","display_name":"Yihao Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Yihao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021492309","display_name":"Jiaming Hu","orcid":"https://orcid.org/0009-0005-6489-7923"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Jiaming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135850834","display_name":"Elmira Amirloo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amirloo, Elmira","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135874986","display_name":"Peter Grasch","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grasch, Peter","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":0.9952999949455261,"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":0.9952999949455261,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0005000000237487257,"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":0.0003000000142492354,"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/closed-captioning","display_name":"Closed captioning","score":0.9854000210762024},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5909000039100647},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5051000118255615},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3856000006198883},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.3447999954223633},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.3091999888420105},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.30070000886917114}],"concepts":[{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.9854000210762024},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8183000087738037},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5909000039100647},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5051000118255615},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4993000030517578},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4609000086784363},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3856000006198883},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.30070000886917114},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2732999920845032},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07394","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07394","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.07394","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07394","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Image":[0],"captioning":[1,140],"is":[2],"one":[3],"of":[4,25,33,61,69,183],"the":[5,23,127],"most":[6],"fundamental":[7],"tasks":[8],"in":[9,22],"computer":[10],"vision.":[11],"Owing":[12],"to":[13,45,124,138],"its":[14],"open-ended":[15],"nature,":[16],"it":[17,145],"has":[18,42],"received":[19],"significant":[20],"attention":[21],"era":[24],"multimodal":[26],"large":[27],"language":[28],"models":[29],"(MLLMs).":[30],"In":[31,122],"pursuit":[32],"ever":[34],"more":[35,108,159],"detailed":[36],"and":[37,53,119,142,167,170,188],"accurate":[38],"captions,":[39],"recent":[40],"work":[41],"increasingly":[43],"turned":[44],"reinforcement":[46],"learning":[47],"(RL).":[48],"However,":[49],"existing":[50],"captioning-RL":[51],"methods":[52],"evaluation":[54],"metrics":[55],"often":[56],"emphasize":[57],"a":[58,107,158],"narrow":[59],"notion":[60],"caption":[62,178],"quality,":[63,179],"inducing":[64],"trade-offs":[65],"across":[66,191],"core":[67],"dimensions":[68],"captioning.":[70,164],"For":[71],"example,":[72],"utility-oriented":[73],"objectives":[74,92],"can":[75,93],"encourage":[76],"noisy,":[77],"hallucinated,":[78],"or":[79],"overlong":[80],"captions":[81],"that":[82,112,144],"improve":[83],"downstream":[84],"question":[85],"answering":[86],"while":[87],"harming":[88],"fluency,":[89],"whereas":[90],"arena-style":[91],"favor":[94],"fluent":[95],"but":[96],"generic":[97],"descriptions":[98],"with":[99,180],"limited":[100],"usefulness.":[101],"To":[102],"address":[103],"this,":[104],"we":[105,133,152],"propose":[106],"balanced":[109],"RL":[110],"framework":[111],"jointly":[113],"optimizes":[114],"utility-aware":[115],"correctness,":[116],"reference":[117],"coverage,":[118],"linguistic":[120],"quality.":[121],"order":[123],"effectively":[125],"optimize":[126],"resulting":[128],"continuous":[129],"multi-objective":[130],"reward":[131,155],"formulation,":[132],"apply":[134],"GDPO-style":[135],"reward-decoupled":[136],"normalization":[137],"continuous-valued":[139],"rewards":[141],"show":[143],"improves":[146,177],"performance":[147],"over":[148],"vanilla":[149],"GRPO.":[150],"Additionally,":[151],"introduce":[153],"length-conditional":[154],"masking,":[156],"yielding":[157],"suitable":[160],"length":[161],"penalty":[162],"for":[163],"Across":[165],"LLaVA-1.5-7B":[166],"Qwen2.5-VL":[168],"3B":[169],"7B":[171],"base":[172],"models,":[173],"our":[174],"method":[175],"consistently":[176],"peak":[181],"gains":[182],"+13.6":[184],"DCScore,":[185],"+9.0":[186],"CaptionQA,":[187],"+29.0":[189],"CapArena":[190],"different":[192],"models.":[193]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-12T00:00:00"}