{"id":"https://openalex.org/W7164035629","doi":"https://doi.org/10.48550/arxiv.2606.08708","title":"PRPO: Perception-Reinforced Policy Optimization via Token-Level Dynamic Advantage Reshaping","display_name":"PRPO: Perception-Reinforced Policy Optimization via Token-Level Dynamic Advantage Reshaping","publication_year":2026,"publication_date":"2026-06-07","ids":{"openalex":"https://openalex.org/W7164035629","doi":"https://doi.org/10.48550/arxiv.2606.08708"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.08708","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08708","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.2606.08708","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138244605","display_name":"Qiming Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qiming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122940251","display_name":"Tianlun Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Tianlun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138250920","display_name":"Xiaolong Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Xiaolong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138223483","display_name":"Hangyu Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Hangyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138206016","display_name":"Ruiyan Gong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Ruiyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062733750","display_name":"Kangning Niu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niu, Kangning","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061826902","display_name":"Kaitao Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Kaitao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138246466","display_name":"Mu Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Mu","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":0.9455999732017517,"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":0.9455999732017517,"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":0.008299999870359898,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.006599999964237213,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7588000297546387},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5626000165939331},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.4564000070095062},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4081999957561493},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.39089998602867126},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.35420000553131104},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.3538999855518341},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3296000063419342}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7588000297546387},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7542999982833862},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6029000282287598},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5626000165939331},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4821000099182129},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4564000070095062},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4081999957561493},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.39089998602867126},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C85847156","wikidata":"https://www.wikidata.org/wiki/Q59015987","display_name":"Verifiable secret sharing","level":3,"score":0.29260000586509705},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.27480000257492065},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.25429999828338623},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.08708","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08708","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.2606.08708","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08708","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],"with":[2,197],"Verifiable":[3],"Rewards":[4],"(RLVR)":[5],"has":[6],"become":[7],"an":[8],"effective":[9],"paradigm":[10],"for":[11,160,214],"improving":[12],"the":[13,208],"reasoning":[14,107,168],"capability":[15],"of":[16,55,188,210],"Large":[17],"Vision-Language":[18],"Models":[19],"(LVLMs).":[20],"However,":[21],"existing":[22],"RLVR":[23],"methods":[24],"primarily":[25],"rely":[26],"on":[27,137,165],"trajectory-level":[28],"outcome":[29],"rewards,":[30],"which":[31],"assign":[32],"identical":[33],"learning":[34,94],"signals":[35],"across":[36,178],"all":[37],"generated":[38],"tokens.":[39,80,135,162],"This":[40],"coarse-grained":[41],"credit":[42,148,212],"assignment":[43,149,213],"is":[44,57],"fundamentally":[45],"mismatched":[46],"to":[47],"multimodal":[48,106,167,216],"reasoning,":[49],"where":[50],"only":[51],"a":[52,91,115,146],"sparse":[53],"subset":[54],"tokens":[56,67,103,120,155],"causally":[58],"grounded":[59,126],"in":[60],"visual":[61,134],"evidence.":[62],"Consequently,":[63],"these":[64],"pivotal":[65,101],"perceptual":[66,102],"receive":[68],"weak":[69],"supervision":[70],"and":[71,99,127,181,190,201],"are":[72,123],"often":[73],"overwhelmed":[74],"by":[75],"language":[76],"priors":[77],"or":[78,132],"reasoning-template":[79],"To":[81],"address":[82],"this":[83],"limitation,":[84],"we":[85,139],"propose":[86,141],"Perception-Reinforced":[87],"Policy":[88],"Optimization":[89],"(PRPO),":[90],"token-level":[92,147],"reinforcement":[93,217],"framework":[95],"that":[96,118,151,171],"explicitly":[97],"identifies":[98,119],"reinforces":[100],"within":[104],"long-horizon":[105],"trajectories.":[108],"PRPO":[109,172,193],"introduces":[110],"Robust":[111],"Visual":[112],"Dependency":[113],"(RVD),":[114],"principled":[116],"metric":[117],"whose":[121],"predictions":[122],"both":[124,179],"visually":[125],"perturbation-stable,":[128],"filtering":[129],"out":[130],"brittle":[131],"noisy":[133],"Based":[136],"RVD,":[138],"further":[140],"Perceptual":[142],"Advantage":[143],"Reshaping":[144],"(PAR),":[145],"technique":[150],"amplifies":[152],"perceptually":[153],"informative":[154],"while":[156],"preserving":[157],"stable":[158],"gradients":[159],"non-perceptual":[161],"Extensive":[163],"experiments":[164],"seven":[166],"benchmarks":[169],"demonstrate":[170],"consistently":[173],"outperforms":[174],"strong":[175],"LVLM":[176],"baselines":[177],"3B":[180],"7B":[182],"model":[183],"scales,":[184],"achieving":[185],"average":[186],"gains":[187],"23.3%":[189],"21.1%,":[191],"respectively.":[192],"achieves":[194],"state-of-the-art":[195],"performance":[196],"improved":[198],"training":[199],"efficiency":[200],"stronger":[202],"cross-task":[203],"generalization.":[204],"Our":[205],"findings":[206],"highlight":[207],"importance":[209],"fine-grained":[211],"scalable":[215],"learning.":[218]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}