{"id":"https://openalex.org/W7160960353","doi":"https://doi.org/10.48550/arxiv.2605.08817","title":"How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors","display_name":"How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors","publication_year":2026,"publication_date":"2026-05-09","ids":{"openalex":"https://openalex.org/W7160960353","doi":"https://doi.org/10.48550/arxiv.2605.08817"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.08817","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08817","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.2605.08817","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135924547","display_name":"Yifan Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135928866","display_name":"Junren Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Junren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135954567","display_name":"Yifan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yifan","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":0.2094999998807907,"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":0.2094999998807907,"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":0.18860000371932983,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.17890000343322754,"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/prior-probability","display_name":"Prior probability","score":0.6216999888420105},{"id":"https://openalex.org/keywords/verifiable-secret-sharing","display_name":"Verifiable secret sharing","score":0.5570999979972839},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.5285000205039978},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5029000043869019},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.46389999985694885},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.45179998874664307},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.3808000087738037},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.3528999984264374}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6891000270843506},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6216999888420105},{"id":"https://openalex.org/C85847156","wikidata":"https://www.wikidata.org/wiki/Q59015987","display_name":"Verifiable secret sharing","level":3,"score":0.5570999979972839},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5467000007629395},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.5285000205039978},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5029000043869019},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.46389999985694885},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.45179998874664307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40209999680519104},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3808000087738037},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.3116999864578247},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.311599999666214},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.29910001158714294},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C2985793214","wikidata":"https://www.wikidata.org/wiki/Q3274096","display_name":"Utility maximization","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C141603448","wikidata":"https://www.wikidata.org/wiki/Q134830","display_name":"Prefix","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C2779795794","wikidata":"https://www.wikidata.org/wiki/Q7315343","display_name":"Reset (finance)","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.08817","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08817","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.2605.08817","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08817","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reinforcement":[0],"learning":[1],"with":[2,184],"verifiable":[3,148],"rewards":[4,149],"(RLVR)":[5],"recently":[6],"thrives":[7],"in":[8,64,155,189,193],"large":[9],"language":[10],"model":[11],"(LLM)":[12],"reasoning":[13,22,50,94,136,179],"tasks.":[14],"However,":[15],"the":[16,20,34,89,107,125,147],"reward":[17,144],"sparsity":[18],"and":[19,134,158,191],"long":[21],"horizon":[23],"make":[24],"effective":[25],"exploration":[26,53,103],"challenging.":[27],"In":[28,67],"practice,":[29],"this":[30,70],"challenge":[31],"manifests":[32],"as":[33,113],"\\emph{entropy":[35],"collapse}":[36],"phenomenon,":[37],"where":[38],"RLVR":[39,165],"improves":[40,178],"single-rollout":[41],"accuracy":[42],"but":[43],"fails":[44],"to":[45,59,69,80,101,145,187],"expand":[46],"coverage":[47],"on":[48,99,104],"successful":[49],"trajectories.":[51,95],"Passive":[52],"techniques":[54],"like":[55],"entropy":[56],"regularization":[57],"tend":[58],"dismiss":[60],"generation":[61],"quality,":[62],"resulting":[63],"noisy":[65],"rollouts.":[66],"response":[68],"issue,":[71],"we":[72,138],"propose":[73],"an":[74,140],"Information-Maximizing":[75],"Augmented":[76],"eXploration":[77],"(IMAX)":[78],"framework":[79],"train":[81],"a":[82,114,120],"pool":[83],"of":[84,106,132],"soft":[85],"prefixes":[86],"that":[87,118,171],"reshapes":[88],"base":[90,108],"model's":[91],"prior":[92],"over":[93,181],"Rather":[96],"than":[97],"relying":[98],"RL":[100,151],"incentivize":[102],"top":[105],"model,":[109],"each":[110],"prefix":[111],"acts":[112],"trainable":[115],"control":[116],"knob":[117],"induces":[119],"distinct":[121],"rollout":[122],"distribution":[123],"from":[124],"same":[126],"backbone":[127,174],"model.":[128],"To":[129],"encourage":[130],"discovery":[131],"diverse":[133],"task-relevant":[135],"behaviors,":[137],"derive":[139],"Information":[141],"Maximization":[142],"(InfoMax)":[143],"complement":[146],"for":[150],"training.":[152],"IMAX":[153,176],"is":[154],"general":[156],"algorithm-agnostic":[157],"can":[159],"be":[160],"seamlessly":[161],"integrated":[162],"into":[163],"existing":[164],"pipelines.":[166],"Experiment":[167],"results":[168],"have":[169],"shown":[170],"across":[172],"three":[173],"scales,":[175],"consistently":[177],"performance":[180],"standard":[182],"RLVR,":[183],"gains":[185],"up":[186],"11.60\\%":[188],"Pass@4":[190],"10.57\\%":[192],"Avg@4.":[194]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}
