{"id":"https://openalex.org/W7153109634","doi":"https://doi.org/10.48550/arxiv.2604.08178","title":"Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling","display_name":"Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7153109634","doi":"https://doi.org/10.48550/arxiv.2604.08178"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.08178","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08178","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":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.2604.08178","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133363880","display_name":"Jiaxuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiaxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122975076","display_name":"Yulan Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Yulan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113478430","display_name":"Wenjin Yang","orcid":"https://orcid.org/0000-0003-1166-899X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Wenjin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122916902","display_name":"Zheng Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133368188","display_name":"Xin Li","orcid":"https://orcid.org/0000-0002-4976-9406"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5047808444","display_name":"Lan-Zhe Guo","orcid":"https://orcid.org/0000-0001-8965-1288"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Lan-Zhe","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":0.28529998660087585,"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":0.28529998660087585,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.11860000342130661,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10906","display_name":"AI-based Problem Solving and Planning","score":0.08749999850988388,"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/benchmark","display_name":"Benchmark (surveying)","score":0.8062999844551086},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6920999884605408},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6416000127792358},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6215000152587891},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.5952000021934509},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.5324000120162964},{"id":"https://openalex.org/keywords/blueprint","display_name":"Blueprint","score":0.5041000247001648},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.439300000667572}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8062999844551086},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7472000122070312},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6920999884605408},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6416000127792358},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6403999924659729},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6215000152587891},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6187999844551086},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.5952000021934509},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.5324000120162964},{"id":"https://openalex.org/C155911762","wikidata":"https://www.wikidata.org/wiki/Q422321","display_name":"Blueprint","level":2,"score":0.5041000247001648},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3127000033855438},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3100000023841858},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C2779696439","wikidata":"https://www.wikidata.org/wiki/Q7512811","display_name":"Signature (topology)","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2565000057220459}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.08178","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08178","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":"doi:10.48550/arxiv.2604.08178","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08178","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6903396248817444,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"classical":[1],"Reinforcement":[2],"Learning":[3],"from":[4],"Human":[5],"Feedback":[6],"(RLHF),":[7],"Reward":[8],"Models":[9,23],"(RMs)":[10],"serve":[11,194],"as":[12,195],"the":[13,36,47,180],"fundamental":[14],"signal":[15],"provider":[16],"for":[17,182,205],"model":[18],"alignment.":[19],"As":[20],"Large":[21],"Language":[22],"evolve":[24],"into":[25],"agentic":[26,207],"systems":[27],"capable":[28],"of":[29,38,49,157],"autonomous":[30],"tool":[31],"invocation":[32],"and":[33,104,114,125,135,149,201],"complex":[34,84],"reasoning,":[35],"paradigm":[37],"reward":[39,188],"modeling":[40],"faces":[41],"unprecedented":[42],"challenges":[43],"--":[44,93,109],"most":[45],"notably,":[46],"lack":[48],"benchmarks":[50],"specifically":[51],"designed":[52,71],"to":[53,72,193],"assess":[54],"RM":[55],"capabilities":[56],"within":[57],"tool-integrated":[58],"environments.":[59],"To":[60],"address":[61],"this":[62],"gap,":[63],"we":[64,153],"present":[65],"Plan-RewardBench,":[66],"a":[67,138,197,202],"trajectory-level":[68,187],"preference":[69,209],"benchmark":[70,130],"evaluate":[73],"how":[74],"well":[75],"judges":[76],"distinguish":[77],"preferred":[78],"versus":[79],"distractor":[80],"agent":[81],"trajectories":[82,113],"in":[83,185],"tool-using":[85],"scenarios.":[86],"Plan-RewardBench":[87,191],"covers":[88],"four":[89],"representative":[90,131],"task":[91,150],"families":[92,168],"(i)":[94],"Safety":[95],"Refusal,":[96],"(ii)":[97],"Tool-Irrelevance":[98],"/":[99],"Unavailability,":[100],"(iii)":[101],"Complex":[102],"Planning,":[103],"(iv)":[105],"Robust":[106],"Error":[107],"Recovery":[108],"comprising":[110],"validated":[111],"positive":[112],"confusable":[115],"hard":[116],"negatives":[117],"constructed":[118],"via":[119],"multi-model":[120],"natural":[121],"rollouts,":[122],"rule-based":[123],"perturbations,":[124],"minimal-edit":[126],"LLM":[127],"perturbations.":[128],"We":[129],"RMs":[132],"(generative,":[133],"discriminative,":[134],"LLM-as-Judge)":[136],"under":[137],"unified":[139],"pairwise":[140],"protocol,":[141],"reporting":[142],"accuracy":[143],"trends":[144],"across":[145],"varying":[146],"trajectory":[147],"lengths":[148],"categories.":[151],"Furthermore,":[152],"provide":[154],"diagnostic":[155],"analyses":[156],"prevalent":[158],"failure":[159],"modes.":[160],"Our":[161],"results":[162],"reveal":[163],"that":[164],"all":[165],"three":[166],"evaluator":[167],"face":[169],"substantial":[170],"challenges,":[171],"with":[172],"performance":[173],"degrading":[174],"sharply":[175],"on":[176],"long-horizon":[177],"trajectories,":[178],"underscoring":[179],"necessity":[181],"specialized":[183],"training":[184],"agentic,":[186],"modeling.":[189],"Ultimately,":[190],"aims":[192],"both":[196],"practical":[198],"evaluation":[199],"suite":[200],"reusable":[203],"blueprint":[204],"constructing":[206],"planning":[208],"data.":[210]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-11T00:00:00"}
