{"id":"https://openalex.org/W7133305805","doi":"https://doi.org/10.48550/arxiv.2603.01565","title":"Investigating Group Relative Policy Optimization for Diffusion Transformer based Text-to-Audio Generation","display_name":"Investigating Group Relative Policy Optimization for Diffusion Transformer based Text-to-Audio Generation","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133305805","doi":"https://doi.org/10.48550/arxiv.2603.01565"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01565","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01565","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.01565","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009126718","display_name":"Yi He Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Yi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127883768","display_name":"Yanqing Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yanqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127995615","display_name":"Chen Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127906526","display_name":"Sheng Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Sheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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/T11349","display_name":"Music Technology and Sound Studies","score":0.7440999746322632,"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/T11349","display_name":"Music Technology and Sound Studies","score":0.7440999746322632,"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/T10860","display_name":"Speech and Audio Processing","score":0.027799999341368675,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11309","display_name":"Music and Audio Processing","score":0.024900000542402267,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.7649000287055969},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.6288999915122986},{"id":"https://openalex.org/keywords/high-fidelity","display_name":"High fidelity","score":0.5867000222206116},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5529000163078308},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.44839999079704285},{"id":"https://openalex.org/keywords/sound-quality","display_name":"Sound quality","score":0.4036000072956085}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7710000276565552},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7649000287055969},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.6288999915122986},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.5867000222206116},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5529000163078308},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.44839999079704285},{"id":"https://openalex.org/C167310288","wikidata":"https://www.wikidata.org/wiki/Q7564808","display_name":"Sound quality","level":2,"score":0.4036000072956085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3682999908924103},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31060001254081726},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.2632000148296356}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01565","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01565","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.01565","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01565","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":"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":{"Text-to-audio":[0],"(T2A)":[1],"generation":[2,63],"has":[3],"advanced":[4],"considerably":[5],"in":[6,16,139,160],"recent":[7],"years,":[8],"yet":[9],"existing":[10],"methods":[11],"continue":[12],"to":[13,59,80,112],"face":[14],"challenges":[15],"accurately":[17],"rendering":[18],"complex":[19],"text":[20],"prompts,":[21],"particularly":[22],"those":[23],"involving":[24],"intricate":[25],"audio":[26,85,140,149],"effects,":[27],"and":[28,43,128,142,163],"achieving":[29],"precise":[30],"text-audio":[31,89],"alignment.":[32],"While":[33],"prior":[34],"approaches":[35],"have":[36],"explored":[37],"data":[38],"augmentation,":[39],"explicit":[40],"timing":[41],"conditioning,":[42],"reinforcement":[44,57,109],"learning,":[45],"overall":[46],"synthesis":[47,141,161],"quality":[48],"remains":[49],"constrained.":[50],"In":[51],"this":[52],"work,":[53],"we":[54,131],"experiment":[55],"with":[56,120],"learning":[58,110],"further":[60],"enhance":[61],"T2A":[62,115],"quality,":[64],"building":[65],"on":[66],"diffusion":[67],"transformer":[68],"(DiT)-based":[69],"architectures.":[70],"Our":[71],"method":[72],"first":[73],"employs":[74],"a":[75,106],"large":[76],"language":[77],"model":[78],"(LLM)":[79],"generate":[81],"high-fidelity,":[82],"richly":[83],"detailed":[84],"captions,":[86],"substantially":[87],"improving":[88],"semantic":[90],"alignment,":[91],"especially":[92],"for":[93],"ambiguous":[94],"or":[95],"underspecified":[96],"prompts.":[97],"We":[98],"then":[99],"apply":[100],"Group":[101],"Relative":[102],"Policy":[103],"Optimization":[104],"(GRPO),":[105],"recently":[107],"introduced":[108],"algorithm,":[111],"fine-tune":[113],"the":[114,133],"model.":[116],"Through":[117],"systematic":[118],"experimentation":[119],"diverse":[121],"reward":[122,145],"functions":[123],"(including":[124],"CLAP,":[125],"KL,":[126],"FAD,":[127],"their":[129],"combinations),":[130],"identify":[132],"key":[134],"drivers":[135],"of":[136],"effective":[137],"RL":[138],"analyze":[143],"how":[144],"design":[146],"impacts":[147],"final":[148],"quality.":[150],"Experimental":[151],"results":[152],"demonstrate":[153],"that":[154],"GRPO-based":[155],"fine-tuning":[156],"yield":[157],"substantial":[158],"gains":[159],"fidelity":[162],"prompt":[164],"adherence.":[165]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-04T00:00:00"}
