{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:CUHDU4G7SOMDRZKFAAMSTH4XXW","short_pith_number":"pith:CUHDU4G7","schema_version":"1.0","canonical_sha256":"150e3a70df939838e5450019299f97bd8714ea4a57940ac4a89edfb833d34c54","source":{"kind":"arxiv","id":"2607.08409","version":1},"attestation_state":"computed","paper":{"title":"When Synthetic Speech Is All You Have: Better Call GRPO","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Andreas Stolcke, Esa\\'u Villatoro-Tello, Hasindri Watawana, Kadri Hacio\\u{g}lu, Petr Motlicek, Sergio Burdisso, Shashi Kumar, Yanis Labrak","submitted_at":"2026-07-09T12:34:56Z","abstract_excerpt":"LLM-based ASR adapted to regulated domains such as banking is bottlenecked by privacy: real speech is costly and legally constrained to collect, making synthetic text-to-speech (TTS) an attractive substitute. Yet synthetic speech stays acoustically mismatched with real recordings, and work on this gap has stayed within supervised fine-tuning (SFT). We instead turn to reinforcement learning, and show that Group Relative Policy Optimization (GRPO) extracts far more from the same synthetic speech than SFT. Synthetic-only adaptation of the model with GRPO, a critic-free method rewarding low-WER hy"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.08409","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-09T12:34:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"092995539192df3d6c0b14d48df8469f932f51d710239eac6055428d0f2953e7","abstract_canon_sha256":"17af3e888f2f13d7fef35b4277e452f3139b41528f2b48ba48d995881970730f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-10T01:19:49.725803Z","signature_b64":"YZIVJ7r7TGGTnfa6/5eHwHkURA/nsUjWZ3HQMVAwQyhR+N0yfHH5RJqePB/Cey0fFj6otZrlqYxyZLmd1uacBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"150e3a70df939838e5450019299f97bd8714ea4a57940ac4a89edfb833d34c54","last_reissued_at":"2026-07-10T01:19:49.725375Z","signature_status":"signed_v1","first_computed_at":"2026-07-10T01:19:49.725375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"When Synthetic Speech Is All You Have: Better Call GRPO","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Andreas Stolcke, Esa\\'u Villatoro-Tello, Hasindri Watawana, Kadri Hacio\\u{g}lu, Petr Motlicek, Sergio Burdisso, Shashi Kumar, Yanis Labrak","submitted_at":"2026-07-09T12:34:56Z","abstract_excerpt":"LLM-based ASR adapted to regulated domains such as banking is bottlenecked by privacy: real speech is costly and legally constrained to collect, making synthetic text-to-speech (TTS) an attractive substitute. Yet synthetic speech stays acoustically mismatched with real recordings, and work on this gap has stayed within supervised fine-tuning (SFT). We instead turn to reinforcement learning, and show that Group Relative Policy Optimization (GRPO) extracts far more from the same synthetic speech than SFT. Synthetic-only adaptation of the model with GRPO, a critic-free method rewarding low-WER hy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.08409","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.08409/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.08409","created_at":"2026-07-10T01:19:49.725436+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.08409v1","created_at":"2026-07-10T01:19:49.725436+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.08409","created_at":"2026-07-10T01:19:49.725436+00:00"},{"alias_kind":"pith_short_12","alias_value":"CUHDU4G7SOMD","created_at":"2026-07-10T01:19:49.725436+00:00"},{"alias_kind":"pith_short_16","alias_value":"CUHDU4G7SOMDRZKF","created_at":"2026-07-10T01:19:49.725436+00:00"},{"alias_kind":"pith_short_8","alias_value":"CUHDU4G7","created_at":"2026-07-10T01:19:49.725436+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CUHDU4G7SOMDRZKFAAMSTH4XXW","json":"https://pith.science/pith/CUHDU4G7SOMDRZKFAAMSTH4XXW.json","graph_json":"https://pith.science/api/pith-number/CUHDU4G7SOMDRZKFAAMSTH4XXW/graph.json","events_json":"https://pith.science/api/pith-number/CUHDU4G7SOMDRZKFAAMSTH4XXW/events.json","paper":"https://pith.science/paper/CUHDU4G7"},"agent_actions":{"view_html":"https://pith.science/pith/CUHDU4G7SOMDRZKFAAMSTH4XXW","download_json":"https://pith.science/pith/CUHDU4G7SOMDRZKFAAMSTH4XXW.json","view_paper":"https://pith.science/paper/CUHDU4G7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.08409&json=true","fetch_graph":"https://pith.science/api/pith-number/CUHDU4G7SOMDRZKFAAMSTH4XXW/graph.json","fetch_events":"https://pith.science/api/pith-number/CUHDU4G7SOMDRZKFAAMSTH4XXW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CUHDU4G7SOMDRZKFAAMSTH4XXW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CUHDU4G7SOMDRZKFAAMSTH4XXW/action/storage_attestation","attest_author":"https://pith.science/pith/CUHDU4G7SOMDRZKFAAMSTH4XXW/action/author_attestation","sign_citation":"https://pith.science/pith/CUHDU4G7SOMDRZKFAAMSTH4XXW/action/citation_signature","submit_replication":"https://pith.science/pith/CUHDU4G7SOMDRZKFAAMSTH4XXW/action/replication_record"}},"created_at":"2026-07-10T01:19:49.725436+00:00","updated_at":"2026-07-10T01:19:49.725436+00:00"}