{"id":"https://openalex.org/W4416050125","doi":"https://doi.org/10.48550/arxiv.2508.14094","title":"Hard Examples Are All You Need: Maximizing GRPO Post-Training Under Annotation Budgets","display_name":"Hard Examples Are All You Need: Maximizing GRPO Post-Training Under Annotation Budgets","publication_year":2025,"publication_date":"2025-08-15","ids":{"openalex":"https://openalex.org/W4416050125","doi":"https://doi.org/10.48550/arxiv.2508.14094"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2508.14094","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.14094","pdf_url":"https://arxiv.org/pdf/2508.14094","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2508.14094","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066372409","display_name":"Benjamin Pikus","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pikus, Benjamin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Tiwari, Pratyush Ranjan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tiwari, Pratyush Ranjan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100628591","display_name":"Bin Ye","orcid":"https://orcid.org/0000-0002-7791-5263"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Burton","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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":0.3084000051021576,"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":0.3084000051021576,"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/T13629","display_name":"Text Readability and Simplification","score":0.13449999690055847,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.12099999934434891,"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/limiting","display_name":"Limiting","score":0.6162999868392944},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6087999939918518},{"id":"https://openalex.org/keywords/base","display_name":"Base (topology)","score":0.49900001287460327},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.4805000126361847},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.4708999991416931},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.42579999566078186},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.41839998960494995},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.40939998626708984},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.40689998865127563}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7523000240325928},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.6162999868392944},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6087999939918518},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5580999851226807},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5491999983787537},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.49900001287460327},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.4805000126361847},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.4708999991416931},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.42579999566078186},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.41839998960494995},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.40939998626708984},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.40689998865127563},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.36160001158714294},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.27250000834465027},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C2776372474","wikidata":"https://www.wikidata.org/wiki/Q508291","display_name":"Simplicity","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C2777868144","wikidata":"https://www.wikidata.org/wiki/Q7239817","display_name":"Preference elicitation","level":3,"score":0.2531999945640564}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2508.14094","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.14094","pdf_url":"https://arxiv.org/pdf/2508.14094","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2508.14094","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.14094","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2508.14094","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.14094","pdf_url":"https://arxiv.org/pdf/2508.14094","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"Collecting":[0],"high-quality":[1],"training":[2,29,93],"examples":[3,51,69,87,96,110,137],"for":[4],"language":[5],"model":[6,56,141],"fine-tuning":[7,152],"is":[8],"expensive,":[9],"with":[10,115],"practical":[11],"budgets":[12],"limiting":[13],"the":[14,47,54,123],"amount":[15],"of":[16,50,73],"data":[17],"that":[18],"can":[19],"be":[20],"procured.":[21],"We":[22],"investigate":[23],"whether":[24],"example":[25],"difficulty":[26],"affects":[27],"GRPO":[28,78,151],"effectiveness":[30],"by":[31],"comparing":[32],"selection":[33],"strategies":[34],"(easy,":[35],"medium,":[36],"hard,":[37],"random)":[38],"across":[39],"multiple":[40],"models":[41,106,118],"and":[42,135],"reasoning":[43],"tasks.":[44],"Training":[45],"on":[46,108,122],"hardest":[48],"10\\%":[49],"(those":[52],"where":[53,138],"base":[55,140],"fails":[57],"most":[58],"often)":[59],"yields":[60],"dramatic":[61],"performance":[62],"gains":[63,121],"up":[64],"to":[65,82,99],"47\\%,":[66],"while":[67,94],"easy":[68,95],"produce":[70],"minimal":[71],"improvements":[72],"3-15\\%.":[74],"This":[75],"occurs":[76],"because":[77],"requires":[79],"outcome":[80],"variance":[81],"generate":[83],"learning":[84,103,148],"signals;":[85],"hard":[86,109],"maintain":[88],"mixed":[89],"success/failure":[90],"outcomes":[91],"throughout":[92],"quickly":[97],"converge":[98],"consistent":[100],"success,":[101],"eliminating":[102],"opportunities.":[104],"Moreover,":[105],"trained":[107],"show":[111],"superior":[112],"out-of-distribution":[113],"generalization,":[114],"only":[116],"hard-trained":[117],"achieving":[119],"meaningful":[120],"AIME2025":[124],"benchmark.":[125],"Our":[126],"findings":[127],"provide":[128],"clear":[129],"guidance:":[130],"when":[131],"budget-constrained,":[132],"prioritize":[133],"collecting":[134],"annotating":[136],"your":[139],"struggles,":[142],"as":[143],"these":[144],"drive":[145],"nearly":[146],"all":[147],"value":[149],"in":[150]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}