{"as_of":"2026-08-17T21:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:57fc62a3d1e2b570697b1247652d18d90b62ba621af34c8962f2a421541a0ac4","coverage":[{"denominator":23,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":23,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T00:35:19.167249Z","state":"measured"},{"denominator":23,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":23,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.11698/citation-record","integrity":"/paper/2608.11698/integrity","json":"/paper/2608.11698/citation-record.json","paper":"/paper/2608.11698"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:35:19.515197Z","title":"On-policy distillation of language mod- els: Learning from self-generated mistakes","venue":null,"work_id":"95befd58-c755-4683-ade9-05390f9605e8","year":2024},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.054951Z"},"links":{"citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:5f2718f9c5fa9de64224a246743335dbf379541ddaf7b915d092b6c9e4c1a6cf","observation_id":"8d2f49b6-af66-4f9a-bea5-9792c9e053c9","resolution":{"observed_at":"2026-08-16T00:35:19.520409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2604.13016","last_updated":"2026-04-15T17:48:51Z","snapshot_observed_at":"2026-08-16T00:27:04.851196Z","submitted_at":"2026-04-14T17:54:28Z","title":"Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.13016","snapshot_observed_at":"2026-08-16T00:35:19.060109Z","title":"Rethink- ing on-policy distillation of large language models: Phe- nomenology, mechanism, and recipe.arXiv preprint arXiv:2604.13016, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.060109Z"},"links":{"cited_paper":"/paper/2604.13016","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:ffc24b65ab5fb13ce2b3833347c13eb29adf454e353a5b9d8d1d02795018b61d","observation_id":"79e2b58e-3191-428c-a018-304fcebf1f0b","resolution":{"observed_at":"2026-08-16T00:35:19.060109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2602.12125","last_updated":"2026-02-26T13:26:22Z","snapshot_observed_at":"2026-08-16T08:35:41.330991Z","submitted_at":"2026-02-12T16:14:29Z","title":"Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2602.12125","snapshot_observed_at":"2026-08-16T00:35:19.075167Z","title":"Learning beyond teacher: Generalized on-policy distillation with reward extrapo- lation.arXiv preprint arXiv:2602.12125, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.075167Z"},"links":{"cited_paper":"/paper/2602.12125","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:83a414ea801866ac8dbfa65a52a52908ff0bd5b7c8216e2f37c81c541308d683","observation_id":"ed9ec3eb-7a5c-4d04-bc22-7cb208b97a1b","resolution":{"observed_at":"2026-08-16T00:35:19.075167Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-08-16T18:00:58.008096Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-16T00:35:19.080208Z","title":"Distill- ing the knowledge in a neural network.arXiv preprint arXiv:1503.02531, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.080208Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:9408bcf6ecb7ff826c14a9cca28cd201ffde43db748cf99cd96e02309bb61805","observation_id":"7f5e6aa7-4a84-4743-a369-f2480d7b71bb","resolution":{"observed_at":"2026-08-16T00:35:19.080208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:35:19.085157Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.085157Z"},"links":{"citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:10ce0e06504e52272c7ce4bd49ca21338453d00eee2d47808ad364e962b3f196","observation_id":"b316f74c-d005-4fb4-8f7c-22dadafd967b","resolution":{"observed_at":"2026-08-16T00:35:19.085157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:35:19.498990Z","title":"MiniLLM: Knowledge distillation of large language mod- els","venue":null,"work_id":"acda22f8-5342-4a3c-af4d-f37a86944cdd","year":2024},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.089748Z"},"links":{"citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:2d343d09312826431ca76d391cf07e5c432d0d305de83f65753755a0d433a080","observation_id":"4949b4f3-3c69-414b-98c1-b74f08159cfe","resolution":{"observed_at":"2026-08-16T00:35:19.504125Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:35:19.094622Z","title":"Model extrapolation expedites alignment","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.094622Z"},"links":{"citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:50ef1d3d8b147dea54d3d937fc32ad0c19171f98771a4d58e4db5e738a270ef1","observation_id":"a8c7552d-97dc-4320-baa3-2eacadd96557","resolution":{"observed_at":"2026-08-16T00:35:19.094622Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:35:19.101059Z","title":"LLM-oriented token-adaptive knowledge distillation.Pro- ceedings of the AAAI Conference on Artificial Intelli- gence, 40(40):34070–34078, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.101059Z"},"links":{"citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:fa047102d4136b050e9c4dee0dc4ad68793c4db06d50d8d4789ad3db270b112f","observation_id":"46898c64-f0ea-44ef-b5db-7c92ab01bc04","resolution":{"observed_at":"2026-08-16T00:35:19.101059Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v40i41.40780","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:35:19.198382Z","title":"ASKD: Reinforcement learning-style knowledge distillation with quality-adaptive skewness.Proceedings of the AAAI Conference on Ar- tificial Intelligence, 40(41):34781–34789, 2026","venue":null,"work_id":"c9a2bc9b-893d-410d-a55d-2945ee3b4169","year":2026},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.105626Z"},"links":{"citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:81483724a070473292f29de80d5068178c88fb43062d33aba8387499008734fd","observation_id":"c23c527f-e31f-4cda-a179-1ae2fdd62788","resolution":{"observed_at":"2026-08-16T00:35:19.205464Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.07804","last_updated":"2026-06-01T07:36:57Z","snapshot_observed_at":"2026-08-16T19:47:17.833856Z","submitted_at":"2026-05-08T14:38:53Z","title":"Prune-OPD: Efficient and Reliable On-Policy Distillation for Long-Horizon Reasoning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.07804","snapshot_observed_at":"2026-08-16T00:35:19.110461Z","title":"Prune-OPD: Efficient and reliable on-policy dis- tillation for long-horizon reasoning.arXiv preprint arXiv:2605.07804, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.110461Z"},"links":{"cited_paper":"/paper/2605.07804","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:68bd780b89f0da8482af63220db6174cd75c90b91c023489f1d9f66fcfb27636","observation_id":"54419194-7d93-41c3-a444-1a01734cd859","resolution":{"observed_at":"2026-08-16T00:35:19.110461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.10688","last_updated":"2026-05-30T16:08:24Z","snapshot_observed_at":"2026-08-14T18:05:40.907227Z","submitted_at":"2026-04-12T15:26:14Z","title":"SCOPE: Signal-Calibrated On-Policy Distillation Enhancement with Dual-Path Adaptive Weighting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.10688","snapshot_observed_at":"2026-08-16T00:35:19.115157Z","title":"SCOPE: Signal-calibrated on-policy dis- tillation enhancement with dual-path adaptive weighting","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.115157Z"},"links":{"cited_paper":"/paper/2604.10688","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:88845953c639f299c124061722493a659eade1848db187396365e4c9a18a9ecf","observation_id":"d5b56c85-1b64-442b-bb03-1b6f5daffe81","resolution":{"observed_at":"2026-08-16T00:35:19.115157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.09304","last_updated":"2026-06-08T10:11:58Z","snapshot_observed_at":"2026-08-12T23:31:46.629708Z","submitted_at":"2026-06-08T10:11:58Z","title":"SG-OPD: Sign-Gated On-Policy Distillation via Sign-Consistency Gating and Phased Teacher Sampling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.09304","snapshot_observed_at":"2026-08-16T00:35:19.120078Z","title":"SG-OPD: Sign-gated on- policy distillation via sign-consistency gating and phased teacher sampling.arXiv preprint arXiv:2606.09304, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.120078Z"},"links":{"cited_paper":"/paper/2606.09304","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:c7ee96d732abc83c9e25fea723bb9b1fb9f806005630fa5765c5f7b0bfe74497","observation_id":"7ab18514-f7ae-4c58-b3cf-b0ca4203e93b","resolution":{"observed_at":"2026-08-16T00:35:19.120078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2607.04037","last_updated":"2026-07-04T21:51:22Z","snapshot_observed_at":"2026-08-16T10:32:10.416257Z","submitted_at":"2026-07-04T21:51:22Z","title":"Reward-Gated On-Policy Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2607.04037","snapshot_observed_at":"2026-08-16T00:35:19.124618Z","title":"Reward-gated on-policy distillation","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.124618Z"},"links":{"cited_paper":"/paper/2607.04037","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:828ae16147b18b271f2dc087f8a1448436c5c2ea3ff86d2f7b132a482083979d","observation_id":"343ca932-becd-49a3-bfbd-a32217317fcd","resolution":{"observed_at":"2026-08-16T00:35:19.124618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-08-15T20:26:32.102285Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-16T00:35:19.129403Z","title":"Proximal policy optimization algorithms.arXiv preprint arXiv:1707.06347, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.129403Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:51f41c3a45d387d5f7982a66c541db9a5f695456c3f65807654124077c0992b8","observation_id":"60ab21a5-297c-438c-b8c9-98d94c2130db","resolution":{"observed_at":"2026-08-16T00:35:19.129403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-08-16T00:35:19.134187Z","title":"Qwen3 Technical Re- port.arXiv preprint arXiv:2505.09388, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.134187Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:309927d692afc4a8e93bdef439d542a0c9b1ef781bf6cba2088d74ec953d2d37","observation_id":"2cc351a7-de7f-42b0-8719-30b5195aac97","resolution":{"observed_at":"2026-08-16T00:35:19.134187Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11456","last_updated":"2025-05-22T19:12:14Z","snapshot_observed_at":"2026-08-13T20:36:12.184426Z","submitted_at":"2025-04-15T17:59:51Z","title":"DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.11456","snapshot_observed_at":"2026-08-16T00:35:19.138789Z","title":"DeepMath-103K: A large- scale, challenging, decontaminated, and verifiable math- ematical dataset for advancing reasoning.arXiv preprint arXiv:2504.11456, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.138789Z"},"links":{"cited_paper":"/paper/2504.11456","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:4597f7ca940f4ca2b3c88bab2cad5a611d5ac11a16696b0dbade408273c2dd25","observation_id":"d455d430-d7af-4006-9d85-f977df22ef75","resolution":{"observed_at":"2026-08-16T00:35:19.138789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.02078","last_updated":"2024-04-02T16:25:30Z","snapshot_observed_at":"2026-08-17T00:42:04.970772Z","submitted_at":"2024-04-02T16:25:30Z","title":"Advancing LLM Reasoning Generalists with Preference Trees","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.02078","snapshot_observed_at":"2026-08-16T00:35:19.143448Z","title":"Advancing LLM reasoning generalists with preference trees.arXiv preprint Preprint– REOPD: Reliability-AdaptiveRew ardExtrapolation forOn-PolicyDistillation 9 arXiv:2404.02078, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.143448Z"},"links":{"cited_paper":"/paper/2404.02078","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:60d7981e3ed0707a043e171bf9aaa51c076b957d63ad55c566fc89fa69c492dc","observation_id":"bd8c6ada-e011-45c3-ac60-530c11e2b87b","resolution":{"observed_at":"2026-08-16T00:35:19.143448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:35:19.483539Z","title":"Decoupled weight de- cay regularization","venue":null,"work_id":"bdbe70ab-377c-48b9-97af-9534f6f0c5b7","year":2019},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.148303Z"},"links":{"citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:2e0c6130f03f9c3b1cc8f91601c04901e1a5b44f8ee4dc7396047e260ffc230e","observation_id":"4df29590-f01d-4cb7-982a-e9b5594f2c56","resolution":{"observed_at":"2026-08-16T00:35:19.488791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-08-08T11:58:24.516369Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-16T00:35:19.152756Z","title":"Evaluating large language models trained on code.arXiv preprint arXiv:2107.03374, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.152756Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:9811d33eed386d625e46d6f0417a3ec445021c154be2eff23065b0e495a04bfe","observation_id":"7b3b7e15-b9d8-4d91-a529-f2e623f53190","resolution":{"observed_at":"2026-08-16T00:35:19.152756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07732","last_updated":"2021-08-16T03:57:30Z","snapshot_observed_at":"2026-08-15T17:40:38.050939Z","submitted_at":"2021-08-16T03:57:30Z","title":"Program Synthesis with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07732","snapshot_observed_at":"2026-08-16T00:35:19.157784Z","title":"Le, and Charles Sutton","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.157784Z"},"links":{"cited_paper":"/paper/2108.07732","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:aad81530dc9835d4563e6453ab7ed35ecfca50e36f6f606a720c36cec9c61a3b","observation_id":"1ec700ea-8cb8-495b-8eb0-b14d17c86318","resolution":{"observed_at":"2026-08-16T00:35:19.157784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.01210","last_updated":"2023-10-30T19:37:09Z","snapshot_observed_at":"2026-08-12T18:11:04.754824Z","submitted_at":"2023-05-02T05:46:48Z","title":"Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.01210","snapshot_observed_at":"2026-08-16T00:35:19.162472Z","title":"Is your code generated by ChatGPT really correct? rigorous evaluation of large language models for code generation.arXiv preprint arXiv:2305.01210, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.162472Z"},"links":{"cited_paper":"/paper/2305.01210","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:09f45c47100c38326af41a73bbb676dd12d76615b1f93d545d1ecc1e1f287376","observation_id":"0e08ed39-0c87-412e-9dba-2d8a1f67fe99","resolution":{"observed_at":"2026-08-16T00:35:19.162472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07974","last_updated":"2024-06-06T17:41:21Z","snapshot_observed_at":"2026-08-16T07:05:57.323612Z","submitted_at":"2024-03-12T17:58:04Z","title":"LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07974","snapshot_observed_at":"2026-08-16T00:35:19.167249Z","title":"LiveCodeBench: Holistic and contamination free evaluation of large language mod- els for code.arXiv preprint arXiv:2403.07974, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.167249Z"},"links":{"cited_paper":"/paper/2403.07974","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:982f6948941ee9252e950dde618cf85f8c9273d40c2e758fcb4400b3d7b00b88","observation_id":"a93e8f58-8d3a-493b-a754-e4b59ae2cf4b","resolution":{"observed_at":"2026-08-16T00:35:19.167249Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.14084","last_updated":"2026-05-21T05:36:13Z","snapshot_observed_at":"2026-08-12T12:04:07.125000Z","submitted_at":"2026-04-15T16:58:24Z","title":"TIP: Token Importance in On-Policy Distillation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.14084","snapshot_observed_at":"2026-08-16T00:35:19.070631Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation","version":2},"reference_index":2026,"source":"pdf_text","source_observed_at":"2026-08-16T00:35:19.070631Z"},"links":{"cited_paper":"/paper/2604.14084","citing_paper":"/paper/2608.11698"},"observation_digest":"sha256:e9acb02cb22d883fa45776c9e1d6f9cc6ea0b64fad7f6116f59e83c3159c0f00","observation_id":"ce6c5471-a501-4538-a6d5-e41177e99e63","resolution":{"observed_at":"2026-08-16T00:35:19.070631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.11698","last_updated":"2026-08-13T01:29:38Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T15:15:03.365026Z","submitted_at":"2026-08-12T06:15:33Z","title":"REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation"},"reference_resolution":{"displayed":23,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":1,"verified_fuzzy":3},"total_outbound_references":23},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2608.11698."}