{"id":"https://openalex.org/W7134283285","doi":"https://doi.org/10.48550/arxiv.2603.05900","title":"Reference-guided Policy Optimization for Molecular Optimization via LLM Reasoning","display_name":"Reference-guided Policy Optimization for Molecular Optimization via LLM Reasoning","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7134283285","doi":"https://doi.org/10.48550/arxiv.2603.05900"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.05900","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128623396","display_name":"Xuan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037648743","display_name":"Zhanke Zhou","orcid":"https://orcid.org/0000-0001-9254-6112"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Zhanke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128606629","display_name":"Zongze Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zongze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128573696","display_name":"Jiangchao Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Jiangchao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128416685","display_name":"Yu Rong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rong, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128529738","display_name":"Lu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Lu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128378768","display_name":"Bo Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Bo","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/T11948","display_name":"Machine Learning in Materials Science","score":0.8766000270843506,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.8766000270843506,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.05040000006556511,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.014299999922513962,"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/context","display_name":"Context (archaeology)","score":0.6039000153541565},{"id":"https://openalex.org/keywords/verifiable-secret-sharing","display_name":"Verifiable secret sharing","score":0.5472999811172485},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.52920001745224},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4966999888420105},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4733000099658966},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.47200000286102295},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4690999984741211},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.43540000915527344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.72079998254776},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6039000153541565},{"id":"https://openalex.org/C85847156","wikidata":"https://www.wikidata.org/wiki/Q59015987","display_name":"Verifiable secret sharing","level":3,"score":0.5472999811172485},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.52920001745224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5097000002861023},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4966999888420105},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4733000099658966},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.47200000286102295},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4690999984741211},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.439300000667572},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.43540000915527344},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4300999939441681},{"id":"https://openalex.org/C173246807","wikidata":"https://www.wikidata.org/wiki/Q7833062","display_name":"Trajectory optimization","level":3,"score":0.41940000653266907},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.375},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3643999993801117},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3490000069141388},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.28200000524520874},{"id":"https://openalex.org/C44616089","wikidata":"https://www.wikidata.org/wiki/Q30158686","display_name":"Constraint satisfaction","level":3,"score":0.2614000141620636},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.05900","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},{"id":"doi:10.48550/arxiv.2603.05900","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05900","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":"pmh:doi:10.48550/arxiv.2603.05900","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"(LLMs)":[3],"benefit":[4],"substantially":[5],"from":[6,105,124],"supervised":[7,166],"fine-tuning":[8],"(SFT)":[9],"and":[10,41,57,76,127,159,181,203,223],"reinforcement":[11],"learning":[12,75],"with":[13,119],"verifiable":[14,132],"rewards":[15,133],"(RLVR)":[16],"in":[17,25,141,164],"reasoning":[18,122,155],"tasks.":[19],"However,":[20],"these":[21],"recipes":[22],"perform":[23],"poorly":[24],"instruction-based":[26],"molecular":[27,192,196],"optimization,":[28],"where":[29],"each":[30,113],"data":[31],"point":[32],"typically":[33],"provides":[34,59],"only":[35,161],"a":[36,165],"single":[37],"optimized":[38],"reference":[39,53,92,106,148],"molecule":[40],"no":[42],"step-by-step":[43],"optimization":[44,101,197,212],"trajectory.":[45],"We":[46],"reveal":[47],"that":[48,103,134],"answer-only":[49],"SFT":[50,202],"on":[51,210],"the":[52,67,81,88,91,125,129,152,162,169,175,211],"molecules":[54,85,107,118],"collapses":[55],"reasoning,":[56],"RLVR":[58,204],"sparse":[60],"feedback":[61],"under":[62,138],"similarity":[63,139],"constraints":[64,140],"due":[65],"to":[66,187,226],"model's":[68],"lack":[69],"of":[70,83,90],"effective":[71],"exploration,":[72,173],"which":[73],"slows":[74],"limits":[77],"optimization.":[78],"To":[79],"encourage":[80],"exploration":[82],"new":[84],"while":[86,174],"balancing":[87],"exploitation":[89],"molecules,":[93],"we":[94],"introduce":[95],"Reference-guided":[96],"Policy":[97],"Optimization":[98],"(RePO),":[99],"an":[100,142],"approach":[102],"learns":[104],"without":[108],"requiring":[109],"trajectory":[110,156],"data.":[111],"At":[112],"update,":[114],"RePO":[115,199],"samples":[116],"candidate":[117],"their":[120],"intermediate":[121,154],"trajectories":[123],"model":[126,130],"trains":[128],"using":[131],"measure":[135],"property":[136],"satisfaction":[137],"RL":[143,170],"manner.":[144,167],"Meanwhile,":[145],"it":[146],"applies":[147],"guidance":[149,176],"by":[150,184],"keeping":[151],"policy's":[153],"as":[157],"context":[158],"training":[160,183],"answer":[163],"Together,":[168],"term":[171,177],"promotes":[172],"mitigates":[178],"reward":[179],"sparsity":[180],"stabilizes":[182],"grounding":[185],"outputs":[186],"references":[188],"when":[189],"many":[190],"valid":[191],"edits":[193],"exist.":[194],"Across":[195],"benchmarks,":[198],"consistently":[200],"outperforms":[201],"baselines":[205],"(e.g.,":[206],"GRPO),":[207],"achieving":[208],"improvements":[209],"metric":[213],"(Success":[214],"Rate":[215],"$\\times$":[216],"Similarity),":[217],"improving":[218],"balance":[219],"across":[220],"competing":[221],"objectives,":[222],"generalizing":[224],"better":[225],"unseen":[227],"instruction":[228],"styles.":[229],"Our":[230],"code":[231],"is":[232],"publicly":[233],"available":[234],"at":[235],"https://github.com/tmlr-group/RePO.":[236]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-10T00:00:00"}