{"id":"https://openalex.org/W7164121332","doi":"https://doi.org/10.48550/arxiv.2606.10528","title":"Representation-Aware Advantage Estimation: Your Reward Model Provides More Than A Scalar Output","display_name":"Representation-Aware Advantage Estimation: Your Reward Model Provides More Than A Scalar Output","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W7164121332","doi":"https://doi.org/10.48550/arxiv.2606.10528"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.10528","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10528","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.2606.10528","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138326459","display_name":"Guozheng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Guozheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138358510","display_name":"Xiyan Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Xiyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138387672","display_name":"Yiwen Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Yiwen","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/T10667","display_name":"Emotion and Mood Recognition","score":0.29980000853538513,"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"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.29980000853538513,"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"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.09679999947547913,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.0908999964594841,"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/encode","display_name":"ENCODE","score":0.6431999802589417},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.548799991607666},{"id":"https://openalex.org/keywords/scalar","display_name":"Scalar (mathematics)","score":0.4607999920845032},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4341000020503998},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.41350001096725464},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.40709999203681946}],"concepts":[{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.6431999802589417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5709999799728394},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.548799991607666},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.4607999920845032},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4422000050544739},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4341000020503998},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.41350001096725464},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.40709999203681946},{"id":"https://openalex.org/C137002209","wikidata":"https://www.wikidata.org/wiki/Q898521","display_name":"Hidden variable theory","level":3,"score":0.38749998807907104},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37459999322891235},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.359499990940094},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31060001254081726},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2870999872684479},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C181204326","wikidata":"https://www.wikidata.org/wiki/Q7239820","display_name":"Preference learning","level":3,"score":0.2653000056743622},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.10528","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10528","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.2606.10528","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10528","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":{"Current":[0],"reinforcement":[1],"learning":[2],"from":[3,13,108],"human":[4],"feedback":[5],"(RLHF)":[6],"methods":[7],"primarily":[8],"rely":[9],"on":[10,136,156,160,166],"scalar":[11,21],"rewards":[12,22],"a":[14,77],"trained":[15],"reward":[16],"model":[17],"(RM).":[18],"While":[19],"effective,":[20],"are":[23,96],"often":[24],"noisy":[25],"and":[26,40,54,84,114,130,132,139,163,180],"fail":[27],"to":[28,82,104,127,153,176],"capture":[29,86],"fine-grained":[30],"preference":[31,41],"differences,":[32],"whereas":[33],"RM":[34,51,91,173],"hidden":[35,52,92],"states":[36,53],"encode":[37],"richer":[38],"semantic":[39],"information.":[42],"We":[43,124],"introduce":[44],"the":[45,67,90],"representation-aware":[46],"advantage":[47,62],"estimation,":[48],"which":[49],"leverages":[50],"models":[55,138],"them":[56],"as":[57,76],"auxiliary":[58],"signals":[59],"for":[60],"better":[61],"estimation.":[63],"Specifically,":[64],"we":[65],"propose":[66],"Graph-based":[68],"Advantage":[69],"Estimation":[70],"(GraphAE),":[71],"treat":[72],"each":[73,102],"sampled":[74],"group":[75],"graph,":[78],"where":[79],"nodes":[80],"correspond":[81],"responses":[83],"edges":[85],"their":[87],"similarity":[88],"in":[89],"space.":[93],"Then":[94],"advantages":[95],"computed":[97],"via":[98],"graph":[99],"propagation,":[100],"enabling":[101],"sample":[103,178],"incorporate":[105],"contextual":[106],"information":[107],"its":[109],"neighbors.":[110],"GraphAE":[111,126],"is":[112],"lightweight":[113],"can":[115],"be":[116],"seamlessly":[117],"integrated":[118],"into":[119],"existing":[120],"group-based":[121],"RL":[122],"algorithms.":[123],"apply":[125],"GRPO,":[128],"GSPO":[129],"RLOO,":[131],"conduct":[133],"extensive":[134],"experiments":[135],"different":[137],"benchmarks.":[140],"Empirical":[141],"results":[142,169],"show":[143],"consistent":[144],"improvements":[145],"across":[146],"three":[147],"benchmarks,":[148],"with":[149],"gains":[150],"of":[151],"up":[152],"+":[154,158,164],"6.3":[155],"Arena-Hard-v0.1,":[157],"8.27":[159],"AlpacaEval":[161],"2.0,":[162],"0.22":[165],"MT-Bench.":[167],"These":[168],"demonstrate":[170],"that":[171],"leveraging":[172],"representations":[174],"leads":[175],"more":[177],"efficient":[179],"robust":[181],"RLHF.":[182]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-11T00:00:00"}
