{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:YWC3IJHJ47CJKCAFEB2EW4RBR2","short_pith_number":"pith:YWC3IJHJ","canonical_record":{"source":{"id":"2510.13786","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-15T17:43:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9487e005a66954e91c32149adfded5424cdd518509be36aa2df7a2394e1bcee8","abstract_canon_sha256":"713ae47eea08fff4bed2b11c38746e1499694d17cccc4516db60778642b19026"},"schema_version":"1.0"},"canonical_sha256":"c585b424e9e7c495080520744b72218e966160a63d15d1223b48ca4c80d67e12","source":{"kind":"arxiv","id":"2510.13786","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.13786","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"arxiv_version","alias_value":"2510.13786v1","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.13786","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"pith_short_12","alias_value":"YWC3IJHJ47CJ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"YWC3IJHJ47CJKCAF","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"YWC3IJHJ","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:YWC3IJHJ47CJKCAFEB2EW4RBR2","target":"record","payload":{"canonical_record":{"source":{"id":"2510.13786","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-15T17:43:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9487e005a66954e91c32149adfded5424cdd518509be36aa2df7a2394e1bcee8","abstract_canon_sha256":"713ae47eea08fff4bed2b11c38746e1499694d17cccc4516db60778642b19026"},"schema_version":"1.0"},"canonical_sha256":"c585b424e9e7c495080520744b72218e966160a63d15d1223b48ca4c80d67e12","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:47.305584Z","signature_b64":"6n2mWpyfTIUSUlXTv4DZHX/DowBwQxU5tvpAhrwEpNhGjjSx3xKef8TfsTbdVzpfw8LvRuvnqD1k9IooBxxLDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c585b424e9e7c495080520744b72218e966160a63d15d1223b48ca4c80d67e12","last_reissued_at":"2026-05-17T23:38:47.304966Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:47.304966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2510.13786","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WNnbEcaiGQLgO3eRAhnlLQYEkMMwEWsbXDnSVklLahxBHdl4DMbbJ+j6oeK23ZODu6UrXUnChxtS4fK3ukkIBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T04:34:59.204291Z"},"content_sha256":"114b7ecb8e4e987789a279f554d1a3e970264a95aab0735daa40d147c0f9e943","schema_version":"1.0","event_id":"sha256:114b7ecb8e4e987789a279f554d1a3e970264a95aab0735daa40d147c0f9e943"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:YWC3IJHJ47CJKCAFEB2EW4RBR2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Art of Scaling Reinforcement Learning Compute for LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"RL training for LLMs follows predictable sigmoidal scaling curves that enable extrapolation from small-scale runs.","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"David Brandfonbrener, Devvrit Khatri, Inderjit S. Dhillon, Lovish Madaan, Manzil Zaheer, Rachit Bansal, Rishabh Agarwal, Rishabh Tiwari, Sai Surya Duvvuri","submitted_at":"2025-10-15T17:43:03Z","abstract_excerpt":"Reinforcement learning (RL) has become central to training large language models (LLMs), yet the field lacks predictive scaling methodologies comparable to those established for pre-training. Despite rapidly rising compute budgets, there is no principled understanding of how to evaluate algorithmic improvements for scaling RL compute. We present the first large-scale systematic study, amounting to more than 400,000 GPU-hours, that defines a principled framework for analyzing and predicting RL scaling in LLMs. We fit sigmoidal compute-performance curves for RL training and ablate a wide range o"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Stable, scalable recipes follow predictable scaling trajectories, enabling extrapolation from smaller-scale runs. We demonstrate its effectiveness by successfully scaling and predicting validation performance on a single RL run scaled up to 100,000 GPU-hours.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the sigmoidal functional form fitted to smaller-scale runs will continue to hold and allow accurate extrapolation at scales an order of magnitude larger, and that the ablated design choices capture the dominant factors that determine asymptotic performance versus efficiency.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A 400k+ GPU-hour study shows RL scaling in LLMs follows predictable sigmoidal trajectories, with most design choices affecting efficiency rather than the performance asymptote, enabling accurate large-scale predictions via the ScaleRL recipe.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"RL training for LLMs follows predictable sigmoidal scaling curves that enable extrapolation from small-scale runs.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c0ca91f5f9cc5a78c459ed2a200d816ed97538917799650b118418451c4e97cf"},"source":{"id":"2510.13786","kind":"arxiv","version":1},"verdict":{"id":"7bf96e1c-3e20-4797-b093-3c83d1d7e01b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T16:24:18.978810Z","strongest_claim":"Stable, scalable recipes follow predictable scaling trajectories, enabling extrapolation from smaller-scale runs. We demonstrate its effectiveness by successfully scaling and predicting validation performance on a single RL run scaled up to 100,000 GPU-hours.","one_line_summary":"A 400k+ GPU-hour study shows RL scaling in LLMs follows predictable sigmoidal trajectories, with most design choices affecting efficiency rather than the performance asymptote, enabling accurate large-scale predictions via the ScaleRL recipe.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the sigmoidal functional form fitted to smaller-scale runs will continue to hold and allow accurate extrapolation at scales an order of magnitude larger, and that the ablated design choices capture the dominant factors that determine asymptotic performance versus efficiency.","pith_extraction_headline":"RL training for LLMs follows predictable sigmoidal scaling curves that enable extrapolation from small-scale runs."},"references":{"count":36,"sample":[{"doi":"","year":2025,"title":"URLhttps://hkunlp.github.io/blog/2025/Polaris. AoPS. AIME problem set 1983-2025,","work_id":"d27693b0-c6ab-488a-958d-31df012bbe1e","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Cwm: An open-weights llm for research on code generation with world models","work_id":"7a74903a-6383-48ce-97b8-17afa5faeae1","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models","work_id":"d4b4aee4-d20f-4572-886a-4ba9ea6c9b81","ref_index":3,"cited_arxiv_id":"2505.22617","is_internal_anchor":true},{"doi":"","year":null,"title":"GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning","work_id":"366607ba-e4ea-4726-98c3-63356e32351c","ref_index":4,"cited_arxiv_id":"2507.01006","is_internal_anchor":true},{"doi":"10.64434/tml.20250910","year":null,"title":"Measuring Mathematical Problem Solving With the MATH Dataset","work_id":"50652ac6-fb7c-4675-a2c2-159c241feb17","ref_index":5,"cited_arxiv_id":"2103.03874","is_internal_anchor":true}],"resolved_work":36,"snapshot_sha256":"077b8abdcdcf67bd5475ecaf9cf9b17b23e5162896a64c2c0f38393b9d31f7bb","internal_anchors":14},"formal_canon":{"evidence_count":3,"snapshot_sha256":"c7617f062677e65d6fe9c9675b38f1fd1a3100eb80adb2c77e4e88fb29f76510"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"7bf96e1c-3e20-4797-b093-3c83d1d7e01b"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RgXrZKpMol3gtPn/IQwUyuZR8Z5J3OTU7xTmRBPcjLfw1uRsAZNYBm9VAoekEPBUJNcmn6VrWeg3R1+W/5DvCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T04:34:59.205377Z"},"content_sha256":"c08e474f0505c34ecd5701ce4fc7c69bc000ab0725c38525486b3b92e976723a","schema_version":"1.0","event_id":"sha256:c08e474f0505c34ecd5701ce4fc7c69bc000ab0725c38525486b3b92e976723a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YWC3IJHJ47CJKCAFEB2EW4RBR2/bundle.json","state_url":"https://pith.science/pith/YWC3IJHJ47CJKCAFEB2EW4RBR2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YWC3IJHJ47CJKCAFEB2EW4RBR2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-24T04:34:59Z","links":{"resolver":"https://pith.science/pith/YWC3IJHJ47CJKCAFEB2EW4RBR2","bundle":"https://pith.science/pith/YWC3IJHJ47CJKCAFEB2EW4RBR2/bundle.json","state":"https://pith.science/pith/YWC3IJHJ47CJKCAFEB2EW4RBR2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YWC3IJHJ47CJKCAFEB2EW4RBR2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YWC3IJHJ47CJKCAFEB2EW4RBR2","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"713ae47eea08fff4bed2b11c38746e1499694d17cccc4516db60778642b19026","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-15T17:43:03Z","title_canon_sha256":"9487e005a66954e91c32149adfded5424cdd518509be36aa2df7a2394e1bcee8"},"schema_version":"1.0","source":{"id":"2510.13786","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.13786","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"arxiv_version","alias_value":"2510.13786v1","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.13786","created_at":"2026-05-17T23:38:47Z"},{"alias_kind":"pith_short_12","alias_value":"YWC3IJHJ47CJ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"YWC3IJHJ47CJKCAF","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"YWC3IJHJ","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:c08e474f0505c34ecd5701ce4fc7c69bc000ab0725c38525486b3b92e976723a","target":"graph","created_at":"2026-05-17T23:38:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Stable, scalable recipes follow predictable scaling trajectories, enabling extrapolation from smaller-scale runs. We demonstrate its effectiveness by successfully scaling and predicting validation performance on a single RL run scaled up to 100,000 GPU-hours."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the sigmoidal functional form fitted to smaller-scale runs will continue to hold and allow accurate extrapolation at scales an order of magnitude larger, and that the ablated design choices capture the dominant factors that determine asymptotic performance versus efficiency."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A 400k+ GPU-hour study shows RL scaling in LLMs follows predictable sigmoidal trajectories, with most design choices affecting efficiency rather than the performance asymptote, enabling accurate large-scale predictions via the ScaleRL recipe."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"RL training for LLMs follows predictable sigmoidal scaling curves that enable extrapolation from small-scale runs."}],"snapshot_sha256":"c0ca91f5f9cc5a78c459ed2a200d816ed97538917799650b118418451c4e97cf"},"formal_canon":{"evidence_count":3,"snapshot_sha256":"c7617f062677e65d6fe9c9675b38f1fd1a3100eb80adb2c77e4e88fb29f76510"},"paper":{"abstract_excerpt":"Reinforcement learning (RL) has become central to training large language models (LLMs), yet the field lacks predictive scaling methodologies comparable to those established for pre-training. Despite rapidly rising compute budgets, there is no principled understanding of how to evaluate algorithmic improvements for scaling RL compute. We present the first large-scale systematic study, amounting to more than 400,000 GPU-hours, that defines a principled framework for analyzing and predicting RL scaling in LLMs. We fit sigmoidal compute-performance curves for RL training and ablate a wide range o","authors_text":"David Brandfonbrener, Devvrit Khatri, Inderjit S. Dhillon, Lovish Madaan, Manzil Zaheer, Rachit Bansal, Rishabh Agarwal, Rishabh Tiwari, Sai Surya Duvvuri","cross_cats":["cs.AI"],"headline":"RL training for LLMs follows predictable sigmoidal scaling curves that enable extrapolation from small-scale runs.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-15T17:43:03Z","title":"The Art of Scaling Reinforcement Learning Compute for LLMs"},"references":{"count":36,"internal_anchors":14,"resolved_work":36,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"URLhttps://hkunlp.github.io/blog/2025/Polaris. AoPS. AIME problem set 1983-2025,","work_id":"d27693b0-c6ab-488a-958d-31df012bbe1e","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Cwm: An open-weights llm for research on code generation with world models","work_id":"7a74903a-6383-48ce-97b8-17afa5faeae1","year":null},{"cited_arxiv_id":"2505.22617","doi":"","is_internal_anchor":true,"ref_index":3,"title":"The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models","work_id":"d4b4aee4-d20f-4572-886a-4ba9ea6c9b81","year":null},{"cited_arxiv_id":"2507.01006","doi":"","is_internal_anchor":true,"ref_index":4,"title":"GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning","work_id":"366607ba-e4ea-4726-98c3-63356e32351c","year":null},{"cited_arxiv_id":"2103.03874","doi":"10.64434/tml.20250910","is_internal_anchor":true,"ref_index":5,"title":"Measuring Mathematical Problem Solving With the MATH Dataset","work_id":"50652ac6-fb7c-4675-a2c2-159c241feb17","year":null}],"snapshot_sha256":"077b8abdcdcf67bd5475ecaf9cf9b17b23e5162896a64c2c0f38393b9d31f7bb"},"source":{"id":"2510.13786","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-16T16:24:18.978810Z","id":"7bf96e1c-3e20-4797-b093-3c83d1d7e01b","model_set":{"reader":"grok-4.3"},"one_line_summary":"A 400k+ GPU-hour study shows RL scaling in LLMs follows predictable sigmoidal trajectories, with most design choices affecting efficiency rather than the performance asymptote, enabling accurate large-scale predictions via the ScaleRL recipe.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"RL training for LLMs follows predictable sigmoidal scaling curves that enable extrapolation from small-scale runs.","strongest_claim":"Stable, scalable recipes follow predictable scaling trajectories, enabling extrapolation from smaller-scale runs. We demonstrate its effectiveness by successfully scaling and predicting validation performance on a single RL run scaled up to 100,000 GPU-hours.","weakest_assumption":"That the sigmoidal functional form fitted to smaller-scale runs will continue to hold and allow accurate extrapolation at scales an order of magnitude larger, and that the ablated design choices capture the dominant factors that determine asymptotic performance versus efficiency."}},"verdict_id":"7bf96e1c-3e20-4797-b093-3c83d1d7e01b"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:114b7ecb8e4e987789a279f554d1a3e970264a95aab0735daa40d147c0f9e943","target":"record","created_at":"2026-05-17T23:38:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"713ae47eea08fff4bed2b11c38746e1499694d17cccc4516db60778642b19026","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-10-15T17:43:03Z","title_canon_sha256":"9487e005a66954e91c32149adfded5424cdd518509be36aa2df7a2394e1bcee8"},"schema_version":"1.0","source":{"id":"2510.13786","kind":"arxiv","version":1}},"canonical_sha256":"c585b424e9e7c495080520744b72218e966160a63d15d1223b48ca4c80d67e12","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c585b424e9e7c495080520744b72218e966160a63d15d1223b48ca4c80d67e12","first_computed_at":"2026-05-17T23:38:47.304966Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:47.304966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6n2mWpyfTIUSUlXTv4DZHX/DowBwQxU5tvpAhrwEpNhGjjSx3xKef8TfsTbdVzpfw8LvRuvnqD1k9IooBxxLDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:47.305584Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.13786","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:114b7ecb8e4e987789a279f554d1a3e970264a95aab0735daa40d147c0f9e943","sha256:c08e474f0505c34ecd5701ce4fc7c69bc000ab0725c38525486b3b92e976723a"],"state_sha256":"8289980b28916621f464b8993b2ed6d88cd46105662a519640ad0e5178c57f79"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SaLb9R19dtvm9imJ1u6cqPqzO9j+pTOzcgcxK80MCRf0RFLu/Gph6FvDGOLhBSnAqc0tVN/dc5v1iG7kVicECA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T04:34:59.210784Z","bundle_sha256":"c42199e71d737cf48e37a189c7b2f24116b910119c922b87e3e0bd379e792e32"}}