{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:R6H35SOKFLENDXN3ADDGNKWUEO","short_pith_number":"pith:R6H35SOK","canonical_record":{"source":{"id":"2511.13649","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-11-17T17:59:54Z","cross_cats_sorted":[],"title_canon_sha256":"5d5aada1fba707d0785c07183b782f75bdf2441e4171fa91b663a1afc32912c1","abstract_canon_sha256":"01d1a7c06bef03a2cf7cb6d0642716c58fbf0c81705d5a3e479fbaceb3363eb5"},"schema_version":"1.0"},"canonical_sha256":"8f8fbec9ca2ac8d1ddbb00c666aad423859da517782b1709711bf019f78f815c","source":{"kind":"arxiv","id":"2511.13649","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.13649","created_at":"2026-07-07T02:17:13Z"},{"alias_kind":"arxiv_version","alias_value":"2511.13649v5","created_at":"2026-07-07T02:17:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.13649","created_at":"2026-07-07T02:17:13Z"},{"alias_kind":"pith_short_12","alias_value":"R6H35SOKFLEN","created_at":"2026-07-07T02:17:13Z"},{"alias_kind":"pith_short_16","alias_value":"R6H35SOKFLENDXN3","created_at":"2026-07-07T02:17:13Z"},{"alias_kind":"pith_short_8","alias_value":"R6H35SOK","created_at":"2026-07-07T02:17:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:R6H35SOKFLENDXN3ADDGNKWUEO","target":"record","payload":{"canonical_record":{"source":{"id":"2511.13649","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-11-17T17:59:54Z","cross_cats_sorted":[],"title_canon_sha256":"5d5aada1fba707d0785c07183b782f75bdf2441e4171fa91b663a1afc32912c1","abstract_canon_sha256":"01d1a7c06bef03a2cf7cb6d0642716c58fbf0c81705d5a3e479fbaceb3363eb5"},"schema_version":"1.0"},"canonical_sha256":"8f8fbec9ca2ac8d1ddbb00c666aad423859da517782b1709711bf019f78f815c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:17:13.730773Z","signature_b64":"AZN89UR9q+J0ctslnsZloVROnrrB2/2GXWwllX2RPQEi1TIj8wKINkboGShwl1vavRE2A+aQSJUvmxCl93H1Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f8fbec9ca2ac8d1ddbb00c666aad423859da517782b1709711bf019f78f815c","last_reissued_at":"2026-07-07T02:17:13.729789Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:17:13.729789Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2511.13649","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-07T02:17:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BqDBBMDQAdjHqBswcKN0n7KHS14QTAnSkSmz1mI4JTeB1HjnvzP+G07kkkL49aylM5o7R2sa/ubWgd+IdlXPBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T00:46:32.844283Z"},"content_sha256":"9bcb08d5f5b9280279f838c7374ac5b97cf4850a7c1fd702702be4c816e61cd9","schema_version":"1.0","event_id":"sha256:9bcb08d5f5b9280279f838c7374ac5b97cf4850a7c1fd702702be4c816e61cd9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:R6H35SOKFLENDXN3ADDGNKWUEO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Distribution Matching Distillation Meets Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Zhang, Changsheng Lu, David Liu, Dengyang Jiang, Dongyang Liu, Harry Yang, Hengzhuang Li, Liuzhuozheng Li, Mengmeng Wang, Peng Gao, Qilong Wu, Steven Hoi, Xin Jin, Zanyi Wang, Zhen Li","submitted_at":"2025-11-17T17:59:54Z","abstract_excerpt":"Distribution Matching Distillation (DMD) facilitates efficient inference by distilling multi-step diffusion models into few-step variants. Concurrently, Reinforcement Learning (RL) has emerged as a vital tool for aligning generative models with human preferences. While both represent critical post-training stages for large-scale diffusion models, existing studies typically treat them as independent, sequential processes, leaving a systematic framework for their unification largely unexplored. In this work, we demonstrate that jointly optimizing these two objectives yields mutual benefits: RL e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.13649","kind":"arxiv","version":5},"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/2511.13649/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-07T02:17:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eCevdJCvZ8l2pwJdtbmaIV5AhzVBNT2YzjnesHqPKH7FpLmWugXqQaxLtU6FFcPLMx6kNXiVgeviL8wo8TsCCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T00:46:32.844866Z"},"content_sha256":"79cd92832492f5267963bdf581dbab9ff82095bb91dc70b47d523ceb242e2dc3","schema_version":"1.0","event_id":"sha256:79cd92832492f5267963bdf581dbab9ff82095bb91dc70b47d523ceb242e2dc3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R6H35SOKFLENDXN3ADDGNKWUEO/bundle.json","state_url":"https://pith.science/pith/R6H35SOKFLENDXN3ADDGNKWUEO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R6H35SOKFLENDXN3ADDGNKWUEO/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":"2026-08-11T00:46:32Z","links":{"resolver":"https://pith.science/pith/R6H35SOKFLENDXN3ADDGNKWUEO","bundle":"https://pith.science/pith/R6H35SOKFLENDXN3ADDGNKWUEO/bundle.json","state":"https://pith.science/pith/R6H35SOKFLENDXN3ADDGNKWUEO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R6H35SOKFLENDXN3ADDGNKWUEO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:R6H35SOKFLENDXN3ADDGNKWUEO","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":"01d1a7c06bef03a2cf7cb6d0642716c58fbf0c81705d5a3e479fbaceb3363eb5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-11-17T17:59:54Z","title_canon_sha256":"5d5aada1fba707d0785c07183b782f75bdf2441e4171fa91b663a1afc32912c1"},"schema_version":"1.0","source":{"id":"2511.13649","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.13649","created_at":"2026-07-07T02:17:13Z"},{"alias_kind":"arxiv_version","alias_value":"2511.13649v5","created_at":"2026-07-07T02:17:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.13649","created_at":"2026-07-07T02:17:13Z"},{"alias_kind":"pith_short_12","alias_value":"R6H35SOKFLEN","created_at":"2026-07-07T02:17:13Z"},{"alias_kind":"pith_short_16","alias_value":"R6H35SOKFLENDXN3","created_at":"2026-07-07T02:17:13Z"},{"alias_kind":"pith_short_8","alias_value":"R6H35SOK","created_at":"2026-07-07T02:17:13Z"}],"graph_snapshots":[{"event_id":"sha256:79cd92832492f5267963bdf581dbab9ff82095bb91dc70b47d523ceb242e2dc3","target":"graph","created_at":"2026-07-07T02:17:13Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2511.13649/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Distribution Matching Distillation (DMD) facilitates efficient inference by distilling multi-step diffusion models into few-step variants. Concurrently, Reinforcement Learning (RL) has emerged as a vital tool for aligning generative models with human preferences. While both represent critical post-training stages for large-scale diffusion models, existing studies typically treat them as independent, sequential processes, leaving a systematic framework for their unification largely unexplored. In this work, we demonstrate that jointly optimizing these two objectives yields mutual benefits: RL e","authors_text":"Bo Zhang, Changsheng Lu, David Liu, Dengyang Jiang, Dongyang Liu, Harry Yang, Hengzhuang Li, Liuzhuozheng Li, Mengmeng Wang, Peng Gao, Qilong Wu, Steven Hoi, Xin Jin, Zanyi Wang, Zhen Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-11-17T17:59:54Z","title":"Distribution Matching Distillation Meets Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.13649","kind":"arxiv","version":5},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9bcb08d5f5b9280279f838c7374ac5b97cf4850a7c1fd702702be4c816e61cd9","target":"record","created_at":"2026-07-07T02:17:13Z","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":"01d1a7c06bef03a2cf7cb6d0642716c58fbf0c81705d5a3e479fbaceb3363eb5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-11-17T17:59:54Z","title_canon_sha256":"5d5aada1fba707d0785c07183b782f75bdf2441e4171fa91b663a1afc32912c1"},"schema_version":"1.0","source":{"id":"2511.13649","kind":"arxiv","version":5}},"canonical_sha256":"8f8fbec9ca2ac8d1ddbb00c666aad423859da517782b1709711bf019f78f815c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8f8fbec9ca2ac8d1ddbb00c666aad423859da517782b1709711bf019f78f815c","first_computed_at":"2026-07-07T02:17:13.729789Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-07T02:17:13.729789Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AZN89UR9q+J0ctslnsZloVROnrrB2/2GXWwllX2RPQEi1TIj8wKINkboGShwl1vavRE2A+aQSJUvmxCl93H1Dg==","signature_status":"signed_v1","signed_at":"2026-07-07T02:17:13.730773Z","signed_message":"canonical_sha256_bytes"},"source_id":"2511.13649","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9bcb08d5f5b9280279f838c7374ac5b97cf4850a7c1fd702702be4c816e61cd9","sha256:79cd92832492f5267963bdf581dbab9ff82095bb91dc70b47d523ceb242e2dc3"],"state_sha256":"72f1c458836042a026c6d3ab2f8efa1fc016758a32a83af8243ea0d4f8885472"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ByBEeRDqlBGeOTSnTCShUBFd38v3V65PWW8O6u2VBITkcPKkxIr/YUhafn6T0Y+eij7qSPIk7kyjNY2ptYDEAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T00:46:32.849701Z","bundle_sha256":"f357c445e7fe1baae36a9e91b5de20b00e02ef1f58328e248d0efa227d7abb82"}}