{"id":"https://openalex.org/W7134073088","doi":"https://doi.org/10.48550/arxiv.2603.04783","title":"Breaking Contextual Inertia: Reinforcement Learning with Single-Turn Anchors for Stable Multi-Turn Interaction","display_name":"Breaking Contextual Inertia: Reinforcement Learning with Single-Turn Anchors for Stable Multi-Turn Interaction","publication_year":2026,"publication_date":"2026-03-05","ids":{"openalex":"https://openalex.org/W7134073088","doi":"https://doi.org/10.48550/arxiv.2603.04783"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.04783","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":"article","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/A5128259232","display_name":"Xingwu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xingwu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128261840","display_name":"Zhanqiu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhanqiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128235963","display_name":"Yiwen Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Yiwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5085848346","display_name":"Difan Zou","orcid":"https://orcid.org/0000-0002-6544-2593"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Difan","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27642276,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.5098000168800354,"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":0.5098000168800354,"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/T10639","display_name":"Advanced Software Engineering Methodologies","score":0.060499999672174454,"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/T10126","display_name":"Logic, programming, and type systems","score":0.04340000078082085,"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/generalization","display_name":"Generalization","score":0.6873000264167786},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.635699987411499},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5515999794006348},{"id":"https://openalex.org/keywords/phenomenon","display_name":"Phenomenon","score":0.5141000151634216},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4821999967098236},{"id":"https://openalex.org/keywords/vulnerability","display_name":"Vulnerability (computing)","score":0.476500004529953},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.3084999918937683}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6873000264167786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6358000040054321},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.635699987411499},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5515999794006348},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5259000062942505},{"id":"https://openalex.org/C50335755","wikidata":"https://www.wikidata.org/wiki/Q483247","display_name":"Phenomenon","level":2,"score":0.5141000151634216},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4821999967098236},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.476500004529953},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3765000104904175},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C2776325391","wikidata":"https://www.wikidata.org/wiki/Q6917865","display_name":"Motivated reasoning","level":3,"score":0.30219998955726624},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2969000041484833},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.2815000116825104},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C110407247","wikidata":"https://www.wikidata.org/wiki/Q122508","display_name":"Inertia","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.266400009393692}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.04783","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.2603.04783","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.04783","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.2603.04783","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"LLMs":[1],"demonstrate":[2],"strong":[3,171],"reasoning":[4,65,91,150],"capabilities":[5,124],"when":[6,23,68],"provided":[7],"with":[8,87,100,137],"full":[9],"information":[10,24],"in":[11,19,42,76],"a":[12,40,57,104],"single":[13],"turn,":[14],"they":[15],"exhibit":[16],"substantial":[17],"vulnerability":[18],"multi-turn":[20,111,135],"interactions.":[21],"Specifically,":[22],"is":[25,192],"revealed":[26],"incrementally":[27],"or":[28,73],"requires":[29],"updates,":[30],"models":[31,60,142],"frequently":[32],"fail":[33],"to":[34,39,45,63,84,109,129,143,176],"integrate":[35],"new":[36,74],"constraints,":[37],"leading":[38],"collapse":[41],"performance":[43],"compared":[44],"their":[46,149],"single-turn":[47,123],"baselines.":[48],"We":[49],"term":[50],"the":[51,79,120,153],"root":[52],"cause":[53],"as":[54,125],"\\emph{Contextual":[55],"Inertia}:":[56],"phenomenon":[58],"where":[59],"rigidly":[61],"adhere":[62],"previous":[64,89],"traces.":[66],"Even":[67],"users":[69],"explicitly":[70],"provide":[71,130],"corrections":[72],"data":[75],"later":[77],"turns,":[78],"model":[80],"ignores":[81],"them,":[82],"preferring":[83],"maintain":[85],"consistency":[86],"its":[88,186],"(incorrect)":[90],"path.":[92],"To":[93],"address":[94],"this,":[95],"we":[96],"introduce":[97],"\\textbf{R}einforcement":[98],"\\textbf{L}earning":[99],"\\textbf{S}ingle-\\textbf{T}urn":[101],"\\textbf{A}nchors":[102],"(\\textbf{RLSTA}),":[103],"generalizable":[105],"training":[106],"approach":[107],"designed":[108],"stabilize":[110],"interaction":[112],"across":[113],"diverse":[114],"scenarios":[115],"and":[116,147,164,178],"domains.":[117],"RLSTA":[118,140,159],"leverages":[119],"model's":[121],"superior":[122],"stable":[126],"internal":[127],"anchors":[128],"reward":[131],"signals.":[132],"By":[133],"aligning":[134],"responses":[136],"these":[138],"anchors,":[139],"empowers":[141],"break":[144],"contextual":[145],"inertia":[146],"self-calibrate":[148],"based":[151],"on":[152],"latest":[154],"information.":[155],"Experiments":[156],"show":[157],"that":[158],"significantly":[160],"outperforms":[161],"standard":[162],"fine-tuning":[163],"abstention-based":[165],"methods.":[166],"Notably,":[167],"our":[168],"method":[169],"exhibits":[170],"cross-domain":[172],"generalization":[173],"(e.g.,":[174],"math":[175],"code)":[177],"proves":[179],"effective":[180],"even":[181],"without":[182],"external":[183],"verifiers,":[184],"highlighting":[185],"potential":[187],"for":[188],"general-domain":[189],"applications.":[190],"Code":[191],"available":[193],"at":[194],"https://github.com/Tencent/RLSTA.":[195]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-03-07T00:00:00"}
