{"id":"https://openalex.org/W7161191422","doi":"https://doi.org/10.48550/arxiv.2605.13165","title":"STOP: Structured On-Policy Pruning of Long-Form Reasoning in Low-Data Regimes","display_name":"STOP: Structured On-Policy Pruning of Long-Form Reasoning in Low-Data Regimes","publication_year":2026,"publication_date":"2026-05-13","ids":{"openalex":"https://openalex.org/W7161191422","doi":"https://doi.org/10.48550/arxiv.2605.13165"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.13165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13165","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":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.2605.13165","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114482526","display_name":"Chenjun Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Chenjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031373151","display_name":"Zhennan Zhou","orcid":"https://orcid.org/0000-0003-4822-0275"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Zhennan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136184705","display_name":"Zhan Su","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Zhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136155252","display_name":"Bill Howe","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Howe, Bill","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136169263","display_name":"Lucy Lu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Lucy Lu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136141835","display_name":"Bingbing Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Bingbing","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.2442999929189682,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.2442999929189682,"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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.1996999979019165,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10028","display_name":"Topic Modeling","score":0.10429999977350235,"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/inference","display_name":"Inference","score":0.6294999718666077},{"id":"https://openalex.org/keywords/backtracking","display_name":"Backtracking","score":0.5555999875068665},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5052000284194946},{"id":"https://openalex.org/keywords/commonsense-reasoning","display_name":"Commonsense reasoning","score":0.47940000891685486},{"id":"https://openalex.org/keywords/reasoning-system","display_name":"Reasoning system","score":0.46630001068115234},{"id":"https://openalex.org/keywords/inefficiency","display_name":"Inefficiency","score":0.4302000105381012},{"id":"https://openalex.org/keywords/non-monotonic-logic","display_name":"Non-monotonic logic","score":0.4262000024318695},{"id":"https://openalex.org/keywords/opportunistic-reasoning","display_name":"Opportunistic reasoning","score":0.40779998898506165},{"id":"https://openalex.org/keywords/automated-reasoning","display_name":"Automated reasoning","score":0.3968999981880188}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.661300003528595},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6294999718666077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5561000108718872},{"id":"https://openalex.org/C156884757","wikidata":"https://www.wikidata.org/wiki/Q798554","display_name":"Backtracking","level":2,"score":0.5555999875068665},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5052000284194946},{"id":"https://openalex.org/C193221554","wikidata":"https://www.wikidata.org/wiki/Q5153664","display_name":"Commonsense reasoning","level":2,"score":0.47940000891685486},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.46630001068115234},{"id":"https://openalex.org/C2778869765","wikidata":"https://www.wikidata.org/wiki/Q6028363","display_name":"Inefficiency","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.4262000024318695},{"id":"https://openalex.org/C86827895","wikidata":"https://www.wikidata.org/wiki/Q7098582","display_name":"Opportunistic reasoning","level":4,"score":0.40779998898506165},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.3968999981880188},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.38749998807907104},{"id":"https://openalex.org/C83725634","wikidata":"https://www.wikidata.org/wiki/Q7268699","display_name":"Qualitative reasoning","level":2,"score":0.37720000743865967},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.3677000105381012},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.36160001158714294},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3424000144004822},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.33399999141693115},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3278000056743622},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.31769999861717224},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C193856179","wikidata":"https://www.wikidata.org/wiki/Q5251100","display_name":"Defeasible estate","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.27140000462532043},{"id":"https://openalex.org/C159423971","wikidata":"https://www.wikidata.org/wiki/Q177251","display_name":"Associative property","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.2567000091075897},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.13165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13165","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":"doi:10.48550/arxiv.2605.13165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13165","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":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Long":[0],"chain-of-thought":[1],"(Long":[2],"CoT)":[3],"reasoning":[4,19,39,72,89,136,192],"improves":[5,183],"performance":[6],"on":[7,47,142],"multi-step":[8],"problems,":[9],"but":[10],"it":[11,82],"also":[12],"induces":[13,175],"overthinking:":[14],"models":[15,40],"often":[16],"generate":[17],"low-yield":[18],"that":[20,120,154,173],"increases":[21],"inference":[22],"cost":[23],"and":[24,44,69,96,127,144,150,190,198],"latency.":[25],"This":[26],"inefficiency":[27],"is":[28],"especially":[29],"problematic":[30],"in":[31,165],"low-data":[32,166],"fine-tuning":[33],"regimes,":[34],"where":[35],"real":[36],"applications":[37],"adapt":[38],"with":[41],"limited":[42],"supervision":[43],"cannot":[45],"rely":[46],"large-scale":[48],"teacher":[49],"distillation":[50],"or":[51],"heavy":[52],"test-time":[53],"control.":[54],"To":[55],"address":[56],"this,":[57],"we":[58,104],"propose":[59],"STOP":[60,74,155,174],"(Structured":[61],"On-policy":[62],"Pruning),":[63],"an":[64,124],"on-policy":[65],"algorithm":[66],"for":[67],"analyzing":[68],"pruning":[70],"long-form":[71],"traces.":[73],"constructs":[75],"self-distilled":[76],"traces":[77],"from":[78,195],"the":[79,112,117,129,184],"model.":[80],"Then":[81],"maps":[83],"each":[84],"trace":[85],"into":[86],"a":[87],"structured":[88],"interface":[90],"through":[91],"node":[92,119],"segmentation,":[93],"taxonomy":[94],"annotation,":[95],"reasoning-tree":[97],"construction.":[98],"On":[99],"top":[100],"of":[101,187],"this":[102],"interface,":[103],"introduce":[105],"ECN":[106],"(Earliest":[107],"Correct":[108],"Node),":[109],"which":[110],"retains":[111],"shortest":[113],"prefix":[114],"ending":[115],"at":[116],"earliest":[118],"both":[121],"functions":[122],"as":[123],"answering":[125],"conclusion":[126],"yields":[128],"correct":[130],"final":[131],"answer,":[132],"removing":[133],"redundant":[134,196],"post-solution":[135],"while":[137,161],"preserving":[138,163],"semantic":[139],"continuity.":[140],"Experiments":[141],"DeepSeek-R1-Distill-Qwen-7B":[143],"DeepSeek-R1-Distill-LLaMA-3-8B":[145],"across":[146],"GSM8K,":[147],"Math":[148],"500,":[149],"AIME":[151],"2024":[152],"show":[153,172],"reduces":[156],"generated":[157,188],"tokens":[158],"by":[159],"19.4-42.4%":[160],"largely":[162],"accuracy":[164],"fine-tuning.":[167],"Beyond":[168],"efficiency,":[169],"our":[170],"analyses":[171],"much":[176],"smaller":[177],"distributional":[178],"shift":[179],"than":[180],"teacher-guided":[181],"pruning,":[182],"structural":[185],"efficiency":[186],"reasoning,":[189],"reallocates":[191],"effort":[193],"away":[194],"verification":[197],"backtracking":[199],"toward":[200],"more":[201],"productive":[202],"exploration.":[203]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-15T00:00:00"}