{"id":"https://openalex.org/W4417492227","doi":"https://doi.org/10.48550/arxiv.2507.15509","title":"Chart-R1: Chain-of-Thought Supervision and Reinforcement for Advanced Chart Reasoner","display_name":"Chart-R1: Chain-of-Thought Supervision and Reinforcement for Advanced Chart Reasoner","publication_year":2025,"publication_date":"2025-07-21","ids":{"openalex":"https://openalex.org/W4417492227","doi":"https://doi.org/10.48550/arxiv.2507.15509"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2507.15509","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.15509","pdf_url":"https://arxiv.org/pdf/2507.15509","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2507.15509","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100689319","display_name":"Lei Chen","orcid":"https://orcid.org/0000-0002-2269-2912"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104174550","display_name":"Xuanle Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Xuanle","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101581937","display_name":"Zhixiong Zeng","orcid":"https://orcid.org/0000-0002-3822-1074"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Zhixiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023965104","display_name":"Jing Huang","orcid":"https://orcid.org/0000-0001-8704-154X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035189764","display_name":"Yufeng Zhong","orcid":"https://orcid.org/0000-0003-2253-1497"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Yufeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100324185","display_name":"Lin Ma","orcid":"https://orcid.org/0000-0002-9810-956X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Lin","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":true,"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":0.15800000727176666,"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":0.15800000727176666,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.14059999585151672,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.12620000541210175,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7979999780654907},{"id":"https://openalex.org/keywords/chart","display_name":"Chart","score":0.7670000195503235},{"id":"https://openalex.org/keywords/semantic-reasoner","display_name":"Semantic reasoner","score":0.6779999732971191},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.41029998660087585},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.337799996137619},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3337000012397766}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7979999780654907},{"id":"https://openalex.org/C190812933","wikidata":"https://www.wikidata.org/wiki/Q28923","display_name":"Chart","level":2,"score":0.7670000195503235},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.756600022315979},{"id":"https://openalex.org/C9616225","wikidata":"https://www.wikidata.org/wiki/Q3929429","display_name":"Semantic reasoner","level":2,"score":0.6779999732971191},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5476999878883362},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4828000068664551},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.41029998660087585},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3337000012397766},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.25540000200271606}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2507.15509","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.15509","pdf_url":"https://arxiv.org/pdf/2507.15509","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2507.15509","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.15509","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":"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":"pmh:oai:arXiv.org:2507.15509","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.15509","pdf_url":"https://arxiv.org/pdf/2507.15509","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4417492227.pdf","grobid_xml":"https://content.openalex.org/works/W4417492227.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Chart":[0],"reasoning":[1,32,73,96],"presents":[2],"unique":[3],"challenges":[4],"due":[5],"to":[6,69],"its":[7],"inherent":[8],"complexity":[9,84],"--":[10],"requiring":[11],"precise":[12],"numerical":[13,40],"comprehension,":[14],"multi-level":[15],"visual":[16],"understanding,":[17],"and":[18,39,83,103,124,137],"logical":[19],"inference":[20],"across":[21],"interconnected":[22],"data":[23,66,74],"elements.":[24],"Existing":[25],"vision-language":[26,51],"models":[27],"often":[28],"struggle":[29],"with":[30,75,112],"such":[31],"tasks,":[33],"particularly":[34],"when":[35],"handling":[36],"multi-subchart":[37],"scenarios":[38],"sensitivity.":[41],"To":[42],"address":[43],"these":[44],"challenges,":[45],"we":[46],"introduce":[47],"Chart-R1,":[48],"a":[49,64],"chart-domain":[50,135],"model":[52],"that":[53,130],"leverages":[54],"reinforcement":[55],"fine-tuning":[56],"for":[57,117],"advanced":[58],"chart":[59,81],"reasoning.":[60,119],"We":[61],"first":[62],"propose":[63],"programmatic":[65],"synthesis":[67],"approach":[68],"generate":[70],"high-quality":[71],"step-by-step":[72],"verifiable":[76],"answer":[77],"formats,":[78],"covering":[79],"diverse":[80],"types":[82],"levels.":[85],"Our":[86],"two-stage":[87],"training":[88],"strategy":[89],"includes:":[90],"(1)":[91],"Chart-COT,":[92],"which":[93,106],"decomposes":[94],"complex":[95],"into":[97],"interpretable":[98],"subtasks":[99],"through":[100],"chain-of-thought":[101],"supervision,":[102],"(2)":[104],"Chart-RFT,":[105],"employs":[107],"group":[108],"relative":[109],"policy":[110],"optimization":[111],"numerically":[113],"sensitive":[114],"rewards":[115],"tailored":[116],"chart-specific":[118],"Experiments":[120],"on":[121],"open-source":[122],"benchmarks":[123],"our":[125],"proposed":[126],"ChartRQA":[127],"dataset":[128],"demonstrate":[129],"Chart-R1":[131],"significantly":[132],"outperforms":[133],"existing":[134],"methods":[136],"rivals":[138],"large-scale":[139],"open/closed-source":[140],"models.":[141]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}
