{"as_of":"2026-08-19T17:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7bd2375b659d6eb3c02e2b44fd91e896258d5e81d75e4153c3c2aa546822dc93","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:10:57.529911Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T07:59:40.183441Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.05193","last_updated":"2024-11-27T00:05:44Z","snapshot_observed_at":"2026-08-16T13:01:55.976106Z","submitted_at":"2024-11-07T21:36:52Z","title":"Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.05193","snapshot_observed_at":"2026-08-15T21:10:57.529911Z","title":"Q-sft: Q-learning for language models via supervised fine-tuning.arXiv preprint arXiv:2411.05193, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11081","last_updated":"2025-05-16T10:12:11Z","snapshot_observed_at":"2026-08-17T20:05:05.299606Z","submitted_at":"2025-05-16T10:12:11Z","title":"ShiQ: Bringing back Bellman to LLMs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T21:10:57.529911Z"},"links":{"cited_paper":"/paper/2411.05193","citing_paper":"/paper/2505.11081"},"observation_digest":"sha256:1fdb61a4a7f32e26a52939b07210b791b189260773a209a52f10ac3090766a99","observation_id":"a5e731ea-e33b-4d1a-b2ac-aa81f4b0a581","resolution":{"observed_at":"2026-08-15T21:10:57.529911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.05193","last_updated":"2024-11-27T00:05:44Z","snapshot_observed_at":"2026-08-16T13:01:55.976106Z","submitted_at":"2024-11-07T21:36:52Z","title":"Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.05193","snapshot_observed_at":"2026-08-07T00:54:49.821661Z","title":"Dragan, and Sergey Levine","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.00018","last_updated":"2025-07-04T08:16:16Z","snapshot_observed_at":"2026-08-17T02:45:12.302961Z","submitted_at":"2025-06-15T05:42:29Z","title":"Implicit Reward as the Bridge: A Unified View of SFT and DPO Connections","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T00:54:49.821661Z"},"links":{"cited_paper":"/paper/2411.05193","citing_paper":"/paper/2507.00018"},"observation_digest":"sha256:239efa35370c8b4e887b747ad808089c83541d0eb10a77a1a055a567a4096377","observation_id":"f97dd5a8-7a18-4ff5-b2f4-fbb033ad99b5","resolution":{"observed_at":"2026-08-07T00:54:49.821661Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.05193","last_updated":"2024-11-27T00:05:44Z","snapshot_observed_at":"2026-08-16T13:01:55.976106Z","submitted_at":"2024-11-07T21:36:52Z","title":"Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.05193","snapshot_observed_at":"2026-08-15T17:52:15.321068Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.20252","last_updated":"2025-08-12T11:22:33Z","snapshot_observed_at":"2026-08-15T20:15:19.196311Z","submitted_at":"2025-07-27T12:47:26Z","title":"Post-Completion Learning for Language Models","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T17:52:15.321068Z"},"links":{"cited_paper":"/paper/2411.05193","citing_paper":"/paper/2507.20252"},"observation_digest":"sha256:4dda33a32793ada5fb3dd00c84c0d6a43b92be27e37e546b32cc630c03d0ee76","observation_id":"46d4ac9f-b08a-458d-abdd-506c69e3831d","resolution":{"observed_at":"2026-08-15T17:52:15.321068Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.05193","last_updated":"2024-11-27T00:05:44Z","snapshot_observed_at":"2026-08-16T13:01:55.976106Z","submitted_at":"2024-11-07T21:36:52Z","title":"Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.05193","snapshot_observed_at":"2026-08-15T15:49:58.162168Z","title":"Q-sft: Q-learning for language models via supervised fine-tuning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.25148","last_updated":"2026-06-18T03:33:32Z","snapshot_observed_at":"2026-08-16T22:17:46.796368Z","submitted_at":"2025-09-29T17:53:09Z","title":"AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T15:49:58.162168Z"},"links":{"cited_paper":"/paper/2411.05193","citing_paper":"/paper/2509.25148"},"observation_digest":"sha256:2a2af59f6b15f7662a13c52e494e763cf2c8c2401aff51bad38fa36e823b7df1","observation_id":"04c91798-d270-49d5-8e48-98e58360054b","resolution":{"observed_at":"2026-08-15T15:49:58.162168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.05193","last_updated":"2024-11-27T00:05:44Z","snapshot_observed_at":"2026-08-16T13:01:55.976106Z","submitted_at":"2024-11-07T21:36:52Z","title":"Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning","version":2},"cited_work":{"arxiv_id":"2411.05193","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.05193","snapshot_observed_at":"2026-07-04T07:59:40.183441Z","title":null,"venue":null,"work_id":"da35bdb4-aac0-4c1e-a406-141d019b979e","year":2024},"citing_paper":{"arxiv_id":"2606.21943","last_updated":"2026-06-20T08:20:41Z","snapshot_observed_at":"2026-08-15T21:55:25.625601Z","submitted_at":"2026-06-20T08:20:41Z","title":"Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-06-26T12:15:08.304150Z"},"links":{"cited_paper":"/paper/2411.05193","citing_paper":"/paper/2606.21943"},"observation_digest":"sha256:0e99dae1ec88e18e4f90045b7a7fed8d2682597fc0b1b300e0313728e1577edf","observation_id":"7bfc2373-26c3-421d-bc93-94b66b9ae816","resolution":{"observed_at":"2026-07-04T07:59:40.184749Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.05193/citation-record","integrity":"/paper/2411.05193/integrity","json":"/paper/2411.05193/citation-record.json","paper":"/paper/2411.05193"},"outbound":[],"paper":{"arxiv_id":"2411.05193","last_updated":"2024-11-27T00:05:44Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T13:01:55.976106Z","submitted_at":"2024-11-07T21:36:52Z","title":"Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2411.05193."}