{"id":"https://openalex.org/W7159653274","doi":"https://doi.org/10.48550/arxiv.2604.27453","title":"From Coarse to Fine: Benchmarking and Reward Modeling for Writing-Centric Generation Tasks","display_name":"From Coarse to Fine: Benchmarking and Reward Modeling for Writing-Centric Generation Tasks","publication_year":2026,"publication_date":"2026-04-30","ids":{"openalex":"https://openalex.org/W7159653274","doi":"https://doi.org/10.48550/arxiv.2604.27453"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.27453","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27453","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":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.2604.27453","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122913726","display_name":"Qingyu Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Qingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081439632","display_name":"Tianjun Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Tianjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039782834","display_name":"Xingzhou Chen","orcid":"https://orcid.org/0000-0003-1631-5202"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xingzhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134937999","display_name":"Xuhong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xuhong","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/T10028","display_name":"Topic Modeling","score":0.5432000160217285,"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/T10028","display_name":"Topic Modeling","score":0.5432000160217285,"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/T13629","display_name":"Text Readability and Simplification","score":0.1046999990940094,"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/T12488","display_name":"Mental Health via Writing","score":0.06350000202655792,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social 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/benchmarking","display_name":"Benchmarking","score":0.7290999889373779},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6121000051498413},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5821999907493591},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4860999882221222},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4772000014781952},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.47450000047683716},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.445499986410141},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.41359999775886536}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7731999754905701},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7290999889373779},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6121000051498413},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5867000222206116},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5821999907493591},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5317000150680542},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4860999882221222},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4772000014781952},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.47450000047683716},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.445499986410141},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.41359999775886536},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3887999951839447},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3856000006198883},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3625999987125397},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3246000111103058},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2703999876976013},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.26080000400543213},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.27453","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27453","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":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.2604.27453","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27453","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8892312049865723}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2,26,74,101,138],"have":[3],"achieved":[4],"remarkable":[5],"progress":[6],"in":[7],"text":[8],"generation":[9],"but":[10],"still":[11],"struggle":[12],"with":[13],"generative":[14],"writing":[15,24,72,99,144],"tasks.":[16],"In":[17,39],"terms":[18,40],"of":[19,36,41,86,98,109],"evaluation,":[20],"existing":[21,43],"benchmarks":[22,145],"evaluate":[23],"reward":[25,53,58,73,100,111,131],"coarsely":[27],"and":[28,75,92,113,119,146,152],"fail":[29],"to":[30],"measure":[31],"performance":[32],"from":[33],"the":[34,104,107,110],"perspective":[35],"specific":[37],"requirements.":[38],"training,":[42],"training":[44,80],"methods":[45],"either":[46],"use":[47],"LLM-as-a-judge":[48],"approaches":[49],"or":[50],"train":[51],"coarse-grained":[52],"models,":[54],"lacking":[55],"fine-grained":[56,67,77],"requirement-adherence":[57],"modeling.":[59],"To":[60],"address":[61],"these":[62],"issues,":[63],"we":[64],"propose":[65],"a":[66,76],"evaluation":[68,84,97],"pipeline":[69],"WEval":[70,87],"for":[71,128],"reinforcement":[78],"learning":[79],"framework":[81],"WRL.":[82],"The":[83,150],"data":[85,153],"covers":[88],"multiple":[89],"task":[90],"categories":[91],"requirement":[93],"types,":[94],"enabling":[95],"systematic":[96],"by":[102,122],"measuring":[103],"correlation":[105],"between":[106],"rankings":[108],"model":[112,132],"gold":[114],"rankings.":[115],"WRL":[116],"constructs":[117],"positive":[118],"negative":[120],"samples":[121],"selectively":[123],"dropping":[124],"instruction":[125],"requirements,":[126],"allowing":[127],"more":[129],"precise":[130],"training.":[133],"Experiments":[134],"show":[135],"that":[136],"our":[137],"achieve":[139],"substantial":[140],"improvements":[141],"across":[142],"various":[143],"exhibit":[147],"strong":[148],"generalization.":[149],"code":[151],"are":[154],"publicly":[155],"available":[156],"at":[157],"\\href{https://github.com/Rainier-rq1/From_Coarse_to_Fine}{https://github.com/Rainier-rq1/From\\_Coarse\\_to\\_Fine}.":[158]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-02T00:00:00"}