{"id":"https://openalex.org/W7160922197","doi":"https://doi.org/10.48550/arxiv.2605.10546","title":"Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning","display_name":"Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7160922197","doi":"https://doi.org/10.48550/arxiv.2605.10546"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.10546","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10546","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.2605.10546","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049788861","display_name":"Raphael Trumpp","orcid":"https://orcid.org/0000-0003-3902-7916"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Trumpp, Raphael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092796585","display_name":"Omer Veysel Cagatan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u00c7a\u011fatan, \u00d6mer Veysel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007552528","display_name":"Bar\u0131\u015f Akg\u00fcn","orcid":"https://orcid.org/0000-0002-4079-6889"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akg\u00fcn, Bar\u0131\u015f","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5060442004","display_name":"Marco Caccamo","orcid":"https://orcid.org/0000-0003-2328-044X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Caccamo, Marco","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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.30889999866485596,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.30889999866485596,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.08169999718666077,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.06599999964237213,"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":0.6686000227928162},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6035000085830688},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.5335000157356262},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4596000015735626},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4564000070095062},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.43130001425743103},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.38999998569488525},{"id":"https://openalex.org/keywords/visual-perception","display_name":"Visual perception","score":0.3668000102043152},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.36169999837875366}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7181000113487244},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6732000112533569},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6686000227928162},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6035000085830688},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.5335000157356262},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4596000015735626},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4564000070095062},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.43130001425743103},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40059998631477356},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.38999998569488525},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.3668000102043152},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.36169999837875366},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3547999858856201},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.34549999237060547},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3425000011920929},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33169999718666077},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2777999937534332},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C2778251979","wikidata":"https://www.wikidata.org/wiki/Q7936617","display_name":"Visual processing","level":3,"score":0.2540999948978424},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.10546","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10546","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.2605.10546","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10546","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":{"Pixel-based":[0],"deep":[1,193,203],"reinforcement":[2],"learning":[3],"agents":[4],"are":[5,136],"typically":[6],"trained":[7],"on":[8,199],"heavily":[9],"downsampled":[10],"visual":[11,88,122,163,192],"observations,":[12],"a":[13,34,72,125,159,186],"convention":[14],"inherited":[15],"from":[16,75,105],"early":[17],"benchmarks":[18],"rather":[19],"than":[20],"grounded":[21],"in":[22,98,138,202],"principled":[23],"design.":[24],"In":[25],"this":[26,91],"work,":[27],"we":[28,205],"show":[29],"that":[30,61,140,154],"observation":[31],"resolution":[32,80,106,200],"is":[33,158],"critical":[35],"yet":[36],"overlooked":[37],"variable":[38],"for":[39,130,211],"policy":[40,167],"learning:":[41],"higher-resolution":[42],"inputs":[43],"can":[44,55],"substantially":[45],"improve":[46],"both":[47],"performance":[48,128],"and":[49,82,107,113,149,181],"generalization,":[50],"provided":[51],"the":[52,62,86,99,155,166,174,208,212],"network":[53,114],"architecture":[54],"process":[56],"them":[57],"effectively.":[58],"We":[59],"find":[60],"widely":[63],"used":[64],"Impala":[65],"encoder,":[66],"which":[67],"flattens":[68],"spatial":[69],"features":[70],"into":[71],"vector,":[73],"suffers":[74],"quadratic":[76],"parameter":[77,103],"growth":[78],"as":[79,97,185],"increases":[81],"fails":[83],"to":[84],"leverage":[85],"additional":[87],"detail.":[89],"Replacing":[90],"operation":[92],"with":[93],"global":[94],"average":[95],"pooling,":[96],"Impoola":[100,131],"architecture,":[101],"decouples":[102],"count":[104],"yields":[108],"consistent":[109],"improvements":[110],"across":[111],"resolutions":[112],"widths":[115],"-":[116],"at":[117,168],"their":[118],"respective":[119],"best":[120],"conditions,":[121],"scaling":[123,201],"unlocks":[124],"28":[126],"%":[127],"gain":[129],"over":[132],"Impala.":[133],"These":[134],"gains":[135],"strongest":[137],"environments":[139],"require":[141],"precise":[142],"perception":[143],"of":[144,165,177],"small":[145],"or":[146],"distant":[147],"objects,":[148],"gradient":[150],"saliency":[151],"analysis":[152],"confirms":[153],"underlying":[156],"mechanism":[157],"more":[160],"spatially":[161],"localized":[162],"attention":[164],"higher":[169],"resolutions.":[170],"Our":[171],"results":[172],"challenge":[173],"prevailing":[175],"practice":[176],"aggressive":[178],"input":[179],"downsampling":[180],"position":[182],"resolution-independent":[183],"architectures":[184],"simple,":[187],"effective":[188],"path":[189],"toward":[190],"scalable":[191],"RL.":[194],"To":[195],"facilitate":[196],"future":[197],"research":[198],"RL,":[204],"publicly":[206],"release":[207],"open-source":[209],"code":[210],"Procgen-HD":[213],"benchmark:":[214],"https://github.com/raphajaner/procgen-hd.":[215]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}