{"id":"https://openalex.org/W7160991704","doi":"https://doi.org/10.48550/arxiv.2605.12004","title":"Learning Agentic Policy from Action Guidance","display_name":"Learning Agentic Policy from Action Guidance","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7160991704","doi":"https://doi.org/10.48550/arxiv.2605.12004"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.12004","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12004","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":null,"license_id":null,"version":null,"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":"https://doi.org/10.48550/arxiv.2605.12004","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136073924","display_name":"Yuxiang Ji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji, Yuxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136041388","display_name":"Zengbin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zengbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136060706","display_name":"Yong Wang","orcid":"https://orcid.org/0000-0001-8473-5386"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136007481","display_name":"Shidong Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Shidong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136086989","display_name":"Ziyu Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Ziyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136005717","display_name":"Guanhua Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Guanhua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136073638","display_name":"Zonghua Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Zonghua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048639585","display_name":"Liaoni Wu","orcid":"https://orcid.org/0000-0002-4558-0462"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Liaoni","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136054372","display_name":"Xiangxiang Chu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chu, Xiangxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"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":0.1574999988079071,"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":0.1574999988079071,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.15000000596046448,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.14910000562667847,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7455000281333923},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6164000034332275},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.6126000285148621},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.609000027179718},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5375000238418579},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4781000018119812},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4449000060558319},{"id":"https://openalex.org/keywords/agency","display_name":"Agency (philosophy)","score":0.3993000090122223},{"id":"https://openalex.org/keywords/intervention","display_name":"Intervention (counseling)","score":0.36629998683929443},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.3528999984264374}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7455000281333923},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6920999884605408},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6164000034332275},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.6126000285148621},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.609000027179718},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5375000238418579},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4819999933242798},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47850000858306885},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4781000018119812},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4449000060558319},{"id":"https://openalex.org/C108170787","wikidata":"https://www.wikidata.org/wiki/Q3951828","display_name":"Agency (philosophy)","level":2,"score":0.3993000090122223},{"id":"https://openalex.org/C2780665704","wikidata":"https://www.wikidata.org/wiki/Q959298","display_name":"Intervention (counseling)","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.34709998965263367},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.3411000072956085},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3370000123977661},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3246000111103058},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.31929999589920044},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3165999948978424},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.30709999799728394},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.2921000123023987},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C166109690","wikidata":"https://www.wikidata.org/wiki/Q4677422","display_name":"Action selection","level":3,"score":0.27469998598098755},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C2776654903","wikidata":"https://www.wikidata.org/wiki/Q2601463","display_name":"SAFER","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C141633740","wikidata":"https://www.wikidata.org/wiki/Q7450996","display_name":"Sense of agency","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.12004","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12004","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.12004","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12004","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.401926726102829}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Agentic":[0],"reinforcement":[1],"learning":[2,48],"(RL)":[3],"for":[4,182],"Large":[5],"Language":[6],"Models":[7],"(LLMs)":[8],"critically":[9],"depends":[10],"on":[11,53,156,161,167,189],"the":[12,16,31,62,83,105,110,121,170,187],"exploration":[13,106],"capability":[14],"of":[15,120],"base":[17,32],"policy,":[18],"as":[19,78,134],"training":[20,39],"signals":[21],"emerge":[22],"only":[23,133],"within":[24],"its":[25],"in-capability":[26],"region.":[27],"For":[28],"tasks":[29],"where":[30],"policy":[33,85],"cannot":[34],"reach":[35],"reward":[36,91],"states,":[37],"additional":[38],"or":[40],"external":[41],"guidance":[42,132,197],"is":[43],"needed":[44],"to":[45,86,90],"recover":[46],"effective":[47],"signals.":[49],"Rather":[50],"than":[51],"relying":[52],"costly":[54],"iterative":[55],"supervised":[56],"fine":[57],"tuning":[58],"(SFT),":[59],"we":[60,124],"exploit":[61],"abundant":[63],"action":[64,76,196],"data":[65,77,192],"generated":[66],"in":[67],"everyday":[68],"human":[69],"interactions.":[70],"We":[71],"propose":[72],"\\textsc{ActGuide-RL},":[73],"which":[74],"injects":[75],"plan-style":[79],"reference":[80],"guidance,":[81],"enabling":[82],"agentic":[84,183],"overcome":[87],"reachability":[88],"barriers":[89],"states.":[92],"Guided":[93],"and":[94,117,158,165],"unguided":[95,111],"rollouts":[96],"are":[97],"then":[98],"jointly":[99],"optimized":[100],"via":[101],"mixed-policy":[102],"training,":[103],"internalizing":[104],"gains":[107],"back":[108],"into":[109],"policy.":[112],"Motivated":[113],"by":[114,193],"a":[115,126,179],"theoretical":[116],"empirical":[118],"analysis":[119],"benefit-risk":[122],"trade-off,":[123],"adopt":[125],"minimal":[127],"intervention":[128],"principle":[129],"that":[130,185],"invokes":[131],"an":[135],"adaptive":[136],"fallback,":[137],"matching":[138],"task":[139],"difficulty":[140],"while":[141],"minimizing":[142],"off-policy":[143],"risk.":[144],"On":[145],"search-agent":[146],"benchmarks,":[147],"\\textsc{ActGuide-RL}":[148],"substantially":[149],"improves":[150],"over":[151],"zero":[152],"RL":[153,184],"(+10.7":[154],"pp":[155,160],"GAIA":[157],"+19":[159],"XBench":[162],"with":[163,169],"Qwen3-4B),":[164],"performs":[166],"par":[168],"SFT+RL":[171],"pipeline":[172],"without":[173],"any":[174],"cold":[175],"start.":[176],"This":[177],"suggests":[178],"new":[180],"paradigm":[181],"reduces":[186],"reliance":[188],"heavy":[190],"SFT":[191],"using":[194],"scalable":[195],"instead.":[198]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-14T00:00:00"}