{"id":"https://openalex.org/W7163222685","doi":"https://doi.org/10.48550/arxiv.2606.01934","title":"HMPO: Hybrid Median-length Policy Optimization for Chain-of-Thought Compression","display_name":"HMPO: Hybrid Median-length Policy Optimization for Chain-of-Thought Compression","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7163222685","doi":"https://doi.org/10.48550/arxiv.2606.01934"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.01934","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01934","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":"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.2606.01934","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066836550","display_name":"Minghui Zheng","orcid":"https://orcid.org/0000-0002-1460-3246"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Minghui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009606065","display_name":"Hongxu Chen","orcid":"https://orcid.org/0000-0001-7963-8813"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Hongxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137707151","display_name":"Huimin Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Huimin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039197077","display_name":"Hongsheng Xin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xin, Hongsheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137697859","display_name":"Xiaoyang Qu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qu, Xiaoyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137702497","display_name":"Ze Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101585896","display_name":"Shuling Yang","orcid":"https://orcid.org/0009-0002-2384-6693"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Shuling","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137661774","display_name":"Ziyu Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Ziyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044154450","display_name":"Kaike Zhang","orcid":"https://orcid.org/0000-0002-1197-5212"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Kaike","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137616675","display_name":"Pan Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Pan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137700777","display_name":"Kun Zhan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhan, Kun","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/T10028","display_name":"Topic Modeling","score":0.19589999318122864,"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":0.19589999318122864,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.1590999960899353,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.05869999900460243,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6722999811172485},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6310999989509583},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6029999852180481},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5674999952316284},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5586000084877014},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.5346999764442444},{"id":"https://openalex.org/keywords/multiplicative-function","display_name":"Multiplicative function","score":0.41670000553131104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7789999842643738},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6722999811172485},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6310999989509583},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6029999852180481},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5674999952316284},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5586000084877014},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.5346999764442444},{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.41670000553131104},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.334199994802475},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3043999969959259},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3025999963283539},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.27000001072883606},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.25029999017715454},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.01934","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01934","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":"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.2606.01934","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01934","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":"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":{"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],"achieve":[3],"remarkable":[4],"performance":[5],"via":[6,58],"extended":[7],"chain-of-thought":[8],"(CoT)":[9],"reasoning,":[10],"yet":[11],"this":[12],"lengthy":[13],"process":[14],"incurs":[15],"substantial":[16],"inference":[17],"overhead.":[18],"Existing":[19],"CoT":[20,57],"compression":[21,133],"methods":[22],"struggle":[23],"with":[24,134],"inflexible":[25],"manual":[26,72],"length":[27,80],"budgets,":[28],"computationally":[29],"expensive":[30],"multi-stage":[31,147],"training":[32,142],"pipelines,":[33],"and":[34,82,110,123],"fragile":[35],"scalability":[36],"restricted":[37],"to":[38,70,118,145],"small":[39],"models.":[40],"We":[41],"propose":[42],"HMPO":[43,54,103,129],"(Hybrid":[44],"Median-length":[45],"Policy":[46],"Optimization),":[47],"a":[48,74,83],"cost-effective,":[49],"single-stage":[50],"reinforcement":[51],"learning":[52],"framework.":[53],"efficiently":[55],"compresses":[56],"three":[59],"synergistic":[60],"components:":[61],"an":[62],"adaptive":[63],"median-based":[64],"budget":[65],"derived":[66],"from":[67,116],"successful":[68],"rollouts":[69],"eliminate":[71],"tuning,":[73],"cosine-decay":[75],"token":[76,132],"reward":[77,85,91],"for":[78],"smooth":[79],"penalization,":[81],"multiplicative":[84],"formulation":[86],"that":[87,128],"substantially":[88],"mitigates":[89],"trivial":[90],"hacking":[92],"by":[93],"strictly":[94],"prioritizing":[95],"answer":[96],"correctness.":[97],"Trained":[98],"exclusively":[99],"on":[100],"mathematical":[101],"data,":[102],"generalizes":[104],"seamlessly":[105],"across":[106,121],"math,":[107],"code,":[108],"science,":[109],"instruction-following":[111],"tasks.":[112],"Extensive":[113],"experiments":[114],"scaling":[115],"9B":[117],"122B":[119],"parameters":[120],"dense":[122],"Mixture-of-Experts":[124],"(MoE)":[125],"architectures":[126],"demonstrate":[127],"achieves":[130],"19%--46%":[131],"negligible":[135],"accuracy":[136],"degradation,":[137],"all":[138],"while":[139],"drastically":[140],"reducing":[141],"costs":[143],"compared":[144],"existing":[146],"baselines.":[148]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-03T00:00:00"}
