{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:RKU3JY4YQ73ERIT3X2RZDZLQ4A","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":"7ccf7f888879734ecc03eec65174e298caecc2b7fff325afd4ab49cf55d217c2","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-24T16:17:18Z","title_canon_sha256":"457ba0670c3eb3a4cc0afdeb24d205d2f8ea91d8aee1fc31ff981cba8fb187bb"},"schema_version":"1.0","source":{"id":"2410.18881","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.18881","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"arxiv_version","alias_value":"2410.18881v2","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.18881","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"pith_short_12","alias_value":"RKU3JY4YQ73E","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"pith_short_16","alias_value":"RKU3JY4YQ73ERIT3","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"pith_short_8","alias_value":"RKU3JY4Y","created_at":"2026-07-05T11:16:30Z"}],"graph_snapshots":[{"event_id":"sha256:08078d3c164327c8170b35975076c9f09e6ee8eafb7bc31fb86a45913e043ea7","target":"graph","created_at":"2026-07-05T11:16:30Z","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/2410.18881/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"One-step text-to-image generator models offer advantages such as swift inference efficiency, flexible architectures, and state-of-the-art generation performance. In this paper, we study the problem of aligning one-step generator models with human preferences for the first time. Inspired by the success of reinforcement learning using human feedback (RLHF), we formulate the alignment problem as maximizing expected human reward functions while adding an Integral Kullback-Leibler divergence term to prevent the generator from diverging. By overcoming technical challenges, we introduce Diff-Instruct","authors_text":"Weijian Luo","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-24T16:17:18Z","title":"Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.18881","kind":"arxiv","version":2},"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:dae4d8871a48f9750f8e6c50513478e5d3bc24d05ea6d9bff34e8fc654517ec6","target":"record","created_at":"2026-07-05T11:16:30Z","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":"7ccf7f888879734ecc03eec65174e298caecc2b7fff325afd4ab49cf55d217c2","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-24T16:17:18Z","title_canon_sha256":"457ba0670c3eb3a4cc0afdeb24d205d2f8ea91d8aee1fc31ff981cba8fb187bb"},"schema_version":"1.0","source":{"id":"2410.18881","kind":"arxiv","version":2}},"canonical_sha256":"8aa9b4e39887f648a27bbea391e570e02476ab0374d18f034844fd0ff57b486f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8aa9b4e39887f648a27bbea391e570e02476ab0374d18f034844fd0ff57b486f","first_computed_at":"2026-07-05T11:16:30.109509Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:16:30.109509Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vOtQqAaNKTaquGGe9IE6nYwofWcx8F6L7y9wv60SpVZUIMccwA8ZII23CxolXyO07GpgwJLTPka3qwzlUiOLAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:16:30.110072Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.18881","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dae4d8871a48f9750f8e6c50513478e5d3bc24d05ea6d9bff34e8fc654517ec6","sha256:08078d3c164327c8170b35975076c9f09e6ee8eafb7bc31fb86a45913e043ea7"],"state_sha256":"6a366e3adcfac360628cbc37ea5f65a68c6e6cbefec98a5c3c7598437e616852"}