{"id":"https://openalex.org/W7165691585","doi":"https://doi.org/10.48550/arxiv.2606.23190","title":"FlowTTS-GRPO: Online Reinforcement Learning with Multi-Objective Reward Optimization for Flow-Matching Based Text-to-Speech","display_name":"FlowTTS-GRPO: Online Reinforcement Learning with Multi-Objective Reward Optimization for Flow-Matching Based Text-to-Speech","publication_year":2026,"publication_date":"2026-06-22","ids":{"openalex":"https://openalex.org/W7165691585","doi":"https://doi.org/10.48550/arxiv.2606.23190"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.23190","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23190","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.23190","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139136134","display_name":"Haoxu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haoxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139177166","display_name":"Biao Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Biao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101414940","display_name":"Weiqing Li","orcid":"https://orcid.org/0000-0002-1929-3654"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Weiqin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139128783","display_name":"Xiang Lv","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lv, Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003608372","display_name":"Hongyun Zhao","orcid":"https://orcid.org/0000-0002-1251-3888"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Han","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139135119","display_name":"Xiangang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiangang","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/T10201","display_name":"Speech Recognition and Synthesis","score":0.46140000224113464,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.46140000224113464,"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/T10860","display_name":"Speech and Audio Processing","score":0.13109999895095825,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.10540000349283218,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7856000065803528},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5921000242233276},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5390999913215637},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5364999771118164},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.49320000410079956},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44920000433921814},{"id":"https://openalex.org/keywords/differential","display_name":"Differential (mechanical device)","score":0.4169999957084656}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7856000065803528},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6933000087738037},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6025000214576721},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5921000242233276},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5390999913215637},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5364999771118164},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.49320000410079956},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4503999948501587},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44920000433921814},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.4169999957084656},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.38440001010894775},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.36160001158714294},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.34779998660087585},{"id":"https://openalex.org/C51955184","wikidata":"https://www.wikidata.org/wiki/Q1545585","display_name":"Stochastic differential equation","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.30730000138282776},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C78045399","wikidata":"https://www.wikidata.org/wiki/Q11214","display_name":"Differential equation","level":2,"score":0.2800999879837036}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.23190","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23190","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.23190","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23190","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":"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":{"Existing":[0],"Reinforcement":[1],"Learning":[2],"(RL)":[3],"research":[4],"for":[5,25],"Text-to-Speech":[6],"(TTS)":[7],"focuses":[8],"on":[9,95],"large":[10],"language":[11],"models":[12,49],"(LLMs),":[13],"leaving":[14],"Flow-Matching":[15],"(FM)":[16],"under-explored.":[17],"We":[18,53],"present":[19],"FlowTTS-GRPO,":[20],"an":[21],"online":[22],"RL":[23,86],"framework":[24],"FM-based":[26],"TTS.":[27],"By":[28],"converting":[29],"ordinary":[30],"differential":[31,37],"equation":[32,38],"(ODE)":[33],"trajectories":[34],"into":[35],"stochastic":[36],"(SDE)":[39],"paths,":[40],"our":[41],"method":[42],"enables":[43],"direct":[44],"fine-tuning":[45],"of":[46],"open-source":[47],"FM":[48,89],"without":[50],"auxiliary":[51],"models.":[52],"show":[54],"that":[55],"a":[56,63],"weighted":[57],"reward":[58],"combination":[59],"converges":[60],"faster":[61],"than":[62],"probabilistic":[64],"scheme,":[65],"and":[66,84,98,102,109],"identify":[67],"three":[68],"practical":[69],"optimizations:":[70],"omitting":[71],"classifier-free":[72],"guidance":[73],"(CFG)":[74],"during":[75],"training":[76],"accelerates":[77],"convergence;":[78],"synthesizing":[79],"hard":[80],"cases":[81],"improves":[82],"robustness;":[83],"applying":[85],"to":[87],"the":[88],"component":[90],"enhances":[91],"audio-detail":[92],"metrics.":[93],"Experiments":[94],"CosyVoice":[96],"3.0":[97],"F5-TTS":[99,113],"demonstrate":[100],"objective":[101],"subjective":[103],"preference":[104],"gains":[105],"in":[106],"speaker":[107],"similarity":[108],"perceptual":[110],"quality,":[111],"with":[112],"also":[114],"improving":[115],"intelligibility.":[116]},"counts_by_year":[],"updated_date":"2026-07-10T05:49:55.623906","created_date":"2026-06-24T00:00:00"}