{"id":"https://openalex.org/W7164206177","doi":"https://doi.org/10.48550/arxiv.2606.10321","title":"Baseline-Free Policy Optimization for Neural Combinatorial Optimization","display_name":"Baseline-Free Policy Optimization for Neural Combinatorial Optimization","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W7164206177","doi":"https://doi.org/10.48550/arxiv.2606.10321"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.10321","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10321","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.2606.10321","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138306843","display_name":"Carlos S. Sep\u00falveda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sep\u00falveda, Carlos S.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136495021","display_name":"Gonzalo A. Ruz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruz, Gonzalo A.","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/T10036","display_name":"Advanced Neural Network Applications","score":0.20280000567436218,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.20280000567436218,"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"}},{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.13580000400543213,"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/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.09830000251531601,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.7376000285148621},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.7361000180244446},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4562999904155731},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.4535999894142151},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.43299999833106995},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.4108999967575073},{"id":"https://openalex.org/keywords/vehicle-routing-problem","display_name":"Vehicle routing problem","score":0.39419999718666077},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.38119998574256897}],"concepts":[{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.7376000285148621},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7361000180244446},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.579800009727478},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5185999870300293},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4562999904155731},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.4535999894142151},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44699999690055847},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.43299999833106995},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.4108999967575073},{"id":"https://openalex.org/C123784306","wikidata":"https://www.wikidata.org/wiki/Q944041","display_name":"Vehicle routing problem","level":3,"score":0.39419999718666077},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.38119998574256897},{"id":"https://openalex.org/C2987595161","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Optimization algorithm","level":2,"score":0.3758000135421753},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.35519999265670776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35120001435279846},{"id":"https://openalex.org/C3017489831","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Running time","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.34929999709129333},{"id":"https://openalex.org/C52692508","wikidata":"https://www.wikidata.org/wiki/Q1333872","display_name":"Combinatorial optimization","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31200000643730164},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.3000999987125397},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.27889999747276306},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C109718341","wikidata":"https://www.wikidata.org/wiki/Q1385229","display_name":"Metaheuristic","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.2680000066757202},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.2678000032901764}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.10321","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10321","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.2606.10321","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10321","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":[{"display_name":"No poverty","id":"https://metadata.un.org/sdg/1","score":0.5546504855155945}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Neural":[0],"combinatorial":[1],"optimization":[2],"(NCO)":[3],"trains":[4],"autoregressive":[5],"policies":[6],"to":[7,118],"solve":[8],"routing":[9],"problems.":[10],"The":[11],"standard":[12],"training":[13,105,193],"algorithm,":[14],"REINFORCE":[15,109],"with":[16,108],"a":[17,25,37,43,82,145,158,182],"rollout":[18],"baseline,":[19,149],"requires":[20],"maintaining":[21],"and":[22,91,125,155],"periodically":[23],"updating":[24],"frozen":[26],"copy":[27],"of":[28,78,85,143],"the":[29,70,95,104,122,164],"policy":[30],"for":[31,186],"variance":[32],"reduction.":[33],"This":[34],"baseline":[35,45,71],"introduces":[36],"structural":[38],"vulnerability:":[39],"on":[40,89,110,169,175],"harder":[41],"instances,":[42],"poor":[44],"produces":[46],"noisy":[47],"gradient":[48,135],"estimates":[49],"that":[50,68],"can":[51],"destabilize":[52],"training.":[53],"We":[54],"evaluate":[55],"Group":[56],"Relative":[57],"Policy":[58],"Optimization":[59],"(GRPO),":[60],"an":[61],"algorithm":[62,161],"from":[63,115,163],"large":[64],"language":[65],"model":[66],"alignment":[67,165],"eliminates":[69],"entirely":[72],"by":[73],"normalizing":[74],"advantages":[75],"within":[76,94,141],"groups":[77],"sampled":[79],"trajectories.":[80],"In":[81],"controlled":[83],"comparison":[84],"five":[86],"RL":[87],"algorithms":[88],"TSP":[90,170],"CVRP":[92],"benchmarks":[93],"RL4CO":[96],"framework,":[97],"we":[98],"find":[99],"that:":[100],"(i)":[101],"GRPO":[102,137,180],"avoids":[103],"collapse":[106],"observed":[107],"TSP-100,":[111],"where":[112,191],"performance":[113],"degrades":[114],"cost":[116],"9.8":[117],"52.1":[119],"immediately":[120],"after":[121],"warmup":[123],"phase":[124],"does":[126],"not":[127],"recover":[128],"under":[129],"extended":[130],"training;":[131],"(ii)":[132],"at":[133],"matched":[134],"updates,":[136],"achieves":[138],"solution":[139],"quality":[140],"2%":[142],"POMO,":[144],"strong":[146],"AM-based":[147],"multi-start":[148],"while":[150],"requiring":[151],"no":[152],"external":[153],"baseline;":[154],"(iii)":[156],"P3O,":[157],"pairwise":[159],"preference":[160],"also":[162],"literature,":[166],"is":[167],"competitive":[168],"but":[171],"shows":[172],"higher":[173],"variability":[174],"CVRP.":[176],"These":[177],"results":[178],"identify":[179],"as":[181],"promising":[183],"baseline-free":[184],"alternative":[185],"NCO,":[187],"particularly":[188],"in":[189],"settings":[190],"baseline-dependent":[192],"becomes":[194],"fragile.":[195]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-11T00:00:00"}