{"id":"https://openalex.org/W7167230771","doi":"https://doi.org/10.48550/arxiv.2607.02118","title":"Enhancing Fitness Intelligence through Domain-Specific LLM Post-Training","display_name":"Enhancing Fitness Intelligence through Domain-Specific LLM Post-Training","publication_year":2026,"publication_date":"2026-07-02","ids":{"openalex":"https://openalex.org/W7167230771","doi":"https://doi.org/10.48550/arxiv.2607.02118"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.02118","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.02118","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":null,"license_id":null,"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.2607.02118","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053213848","display_name":"X Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Xingtao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139980412","display_name":"Tian Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139992337","display_name":"Han Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Han","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":0.3416999876499176,"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":0.3416999876499176,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.0835999995470047,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13629","display_name":"Text Readability and Simplification","score":0.08169999718666077,"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/coaching","display_name":"Coaching","score":0.5734000205993652},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.51419997215271},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.45750001072883606},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4352000057697296},{"id":"https://openalex.org/keywords/subject-matter-expert","display_name":"Subject-matter expert","score":0.43389999866485596},{"id":"https://openalex.org/keywords/certification","display_name":"Certification","score":0.41659998893737793},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.41499999165534973},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4099999964237213}],"concepts":[{"id":"https://openalex.org/C2779363792","wikidata":"https://www.wikidata.org/wiki/Q1104185","display_name":"Coaching","level":2,"score":0.5734000205993652},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.555400013923645},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.51419997215271},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4586000144481659},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.45750001072883606},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4352000057697296},{"id":"https://openalex.org/C105002631","wikidata":"https://www.wikidata.org/wiki/Q4833645","display_name":"Subject-matter expert","level":3,"score":0.43389999866485596},{"id":"https://openalex.org/C46304622","wikidata":"https://www.wikidata.org/wiki/Q374814","display_name":"Certification","level":2,"score":0.41659998893737793},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.41499999165534973},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4099999964237213},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.35920000076293945},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.3151000142097473},{"id":"https://openalex.org/C37228920","wikidata":"https://www.wikidata.org/wiki/Q1307600","display_name":"Experiential learning","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3025999963283539},{"id":"https://openalex.org/C148609458","wikidata":"https://www.wikidata.org/wiki/Q7021281","display_name":"Nexus (standard)","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.29339998960494995},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.28349998593330383},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C2781054738","wikidata":"https://www.wikidata.org/wiki/Q4813730","display_name":"Athletes","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C49929091","wikidata":"https://www.wikidata.org/wiki/Q1930471","display_name":"General knowledge","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C105409693","wikidata":"https://www.wikidata.org/wiki/Q5937824","display_name":"Human intelligence","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C132758656","wikidata":"https://www.wikidata.org/wiki/Q5307365","display_name":"Dreyfus model of skill acquisition","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.02118","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.02118","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.02118","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.02118","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.40304315090179443,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Scientific":[0],"Fitness":[1],"Coaching":[2],"(SFC)":[3],"is":[4,92],"typically":[5],"delivered":[6],"by":[7],"human":[8],"professionals,":[9],"making":[10],"it":[11],"costly":[12],"and":[13,72,79,105,130,141,161,166,212],"inaccessible":[14],"to":[15,52,76,159],"many.":[16],"While":[17],"recent":[18],"advances":[19,204],"in":[20,38,191],"Large":[21],"Language":[22],"Models":[23],"(LLMs)":[24],"show":[25,146],"considerable":[26],"promise":[27],"for":[28,82],"more":[29,208],"inclusive":[30],"fitness":[31,68,125,210],"coaching,":[32],"directly":[33],"deploying":[34],"prevailing":[35],"general-purpose":[36],"LLMs":[37,69],"SFC":[39,57,83],"reveals":[40],"critical":[41],"limitations.":[42],"These":[43],"models":[44],"often":[45],"lack":[46],"sufficient":[47],"domain-specific":[48,219],"knowledge":[49,115,139],"integration,":[50],"leading":[51],"weak":[53],"performance":[54],"on":[55,123,163,217],"complex":[56],"scenarios.":[58],"In":[59],"this":[60,202],"paper,":[61],"we":[62],"introduce":[63],"FitOne,":[64],"a":[65,95],"series":[66],"of":[67,100,121,157,183],"(with":[70],"8B":[71],"32B":[73],"parameters)":[74],"designed":[75],"improve":[77],"reliability":[78],"domain":[80,193],"specialization":[81],"applications.":[84],"Built":[85],"upon":[86],"the":[87,164,172,181,188],"Qwen3":[88,173],"foundation":[89],"models,":[90],"FitOne":[91,122],"developed":[93],"through":[94],"three-stage":[96],"post-training":[97],"pipeline":[98],"consisting":[99],"continual":[101],"pre-training,":[102],"supervised":[103],"fine-tuning,":[104],"reinforcement":[106],"learning,":[107],"using":[108],"large-scale,":[109],"high-quality":[110],"datasets":[111],"derived":[112],"from":[113],"rigorous":[114],"engineering.":[116],"We":[117,200],"conduct":[118],"comprehensive":[119],"evaluations":[120],"professional":[124],"certification":[126],"exams,":[127,168],"including":[128],"ACSM-EP":[129,165],"NSCA-CSCS,":[131],"as":[132,134,138],"well":[133],"general":[135,151,197],"capabilities":[136],"such":[137],"reasoning":[140],"instruction":[142],"following.":[143],"Experimental":[144],"results":[145],"that,":[147],"while":[148],"retaining":[149],"strong":[150],"capabilities,":[152],"FitOne-8B/32B":[153],"achieves":[154],"average":[155],"improvements":[156],"up":[158],"10.09%/9.29%":[160],"12.73%/7.01%":[162],"NSCA-CSCS":[167],"respectively,":[169],"compared":[170],"with":[171,196],"base":[174],"models.":[175],"Furthermore,":[176],"in-depth":[177],"ablation":[178],"studies":[179],"confirm":[180],"necessity":[182],"each":[184],"training":[185],"stage,":[186],"highlighting":[187],"pipeline's":[189],"effectiveness":[190],"balancing":[192],"expertise":[194],"enhancement":[195],"ability":[198],"retention.":[199],"believe":[201],"research":[203,216],"LLM":[205],"systems":[206],"toward":[207],"reliable":[209],"intelligence":[211],"will":[213],"inspire":[214],"future":[215],"developing":[218],"LLMs.":[220]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-04T00:00:00"}
