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Paper Citation Record · LEDGER

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation

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.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.11698 v2

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:35:19.167249Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

  • verified exact1
  • verified fuzzy3
  • unresolved17
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8d2f49b6-af66-4f9a-bea5-9792c9e053c9 · outbound

This paper cites On-policy distillation of language mod- els: Learning from self-generated mistakes.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation On-policy distillation of language mod- els: Learning from self-generated mistakes

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:35:19.054951Z digest=sha256:5f2718f9c5fa9de64224a246743335dbf379541ddaf7b915d092b6c9e4c1a6cf

Observation 79e2b58e-3191-428c-a018-304fcebf1f0b · outbound

This paper cites Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe

Reference 2

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source=pdf_text observed=2026-08-16T00:35:19.060109Z digest=sha256:ffc24b65ab5fb13ce2b3833347c13eb29adf454e353a5b9d8d1d02795018b61d

Observation ed9ec3eb-7a5c-4d04-bc22-7cb208b97a1b · outbound

This paper cites Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation

Reference 4

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source=pdf_text observed=2026-08-16T00:35:19.075167Z digest=sha256:83a414ea801866ac8dbfa65a52a52908ff0bd5b7c8216e2f37c81c541308d683

Observation 7f5e6aa7-4a84-4743-a369-f2480d7b71bb · outbound

This paper cites Distilling the Knowledge in a Neural Network.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Distilling the Knowledge in a Neural Network

Reference 5

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source=pdf_text observed=2026-08-16T00:35:19.080208Z digest=sha256:9408bcf6ecb7ff826c14a9cca28cd201ffde43db748cf99cd96e02309bb61805

Observation b316f74c-d005-4fb4-8f7c-22dadafd967b · outbound

This paper cites an unresolved cited work.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Unresolved cited work

Reference 6

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source=pdf_text observed=2026-08-16T00:35:19.085157Z digest=sha256:10ce0e06504e52272c7ce4bd49ca21338453d00eee2d47808ad364e962b3f196

Observation 4949b4f3-3c69-414b-98c1-b74f08159cfe · outbound

This paper cites MiniLLM: Knowledge distillation of large language mod- els.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation MiniLLM: Knowledge distillation of large language mod- els

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:35:19.089748Z digest=sha256:2d343d09312826431ca76d391cf07e5c432d0d305de83f65753755a0d433a080

Observation a8c7552d-97dc-4320-baa3-2eacadd96557 · outbound

This paper cites Model extrapolation expedites alignment.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Model extrapolation expedites alignment

Reference 8

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source=pdf_text observed=2026-08-16T00:35:19.094622Z digest=sha256:50ef1d3d8b147dea54d3d937fc32ad0c19171f98771a4d58e4db5e738a270ef1

Observation 46898c64-f0ea-44ef-b5db-7c92ab01bc04 · outbound

This paper cites LLM-oriented token-adaptive knowledge distillation.Pro- ceedings of the AAAI Conference on Artificial Intelli- gence, 40(40):34070–34078, 2026.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation LLM-oriented token-adaptive knowledge distillation.Pro- ceedings of the AAAI Conference on Artificial Intelli- gence, 40(40):34070–34078, 2026

Reference 9

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source=pdf_text observed=2026-08-16T00:35:19.101059Z digest=sha256:fa047102d4136b050e9c4dee0dc4ad68793c4db06d50d8d4789ad3db270b112f

Observation c23c527f-e31f-4cda-a179-1ae2fdd62788 · outbound

This paper cites ASKD: Reinforcement learning-style knowledge distillation with quality-adaptive skewness.Proceedings of the AAAI Conference on Ar- tificial Intelligence, 40(41):34781–34789, 2026.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation ASKD: Reinforcement learning-style knowledge distillation with quality-adaptive skewness.Proceedings of the AAAI Conference on Ar- tificial Intelligence, 40(41):34781–34789, 2026

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:35:19.105626Z digest=sha256:81483724a070473292f29de80d5068178c88fb43062d33aba8387499008734fd

Observation 54419194-7d93-41c3-a444-1a01734cd859 · outbound

This paper cites Prune-OPD: Efficient and Reliable On-Policy Distillation for Long-Horizon Reasoning.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Prune-OPD: Efficient and Reliable On-Policy Distillation for Long-Horizon Reasoning

Reference 11

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source=pdf_text observed=2026-08-16T00:35:19.110461Z digest=sha256:68bd780b89f0da8482af63220db6174cd75c90b91c023489f1d9f66fcfb27636

Observation d5b56c85-1b64-442b-bb03-1b6f5daffe81 · outbound

This paper cites SCOPE: Signal-Calibrated On-Policy Distillation Enhancement with Dual-Path Adaptive Weighting.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation SCOPE: Signal-Calibrated On-Policy Distillation Enhancement with Dual-Path Adaptive Weighting

Reference 12

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source=pdf_text observed=2026-08-16T00:35:19.115157Z digest=sha256:88845953c639f299c124061722493a659eade1848db187396365e4c9a18a9ecf

Observation 7ab18514-f7ae-4c58-b3cf-b0ca4203e93b · outbound

This paper cites SG-OPD: Sign-Gated On-Policy Distillation via Sign-Consistency Gating and Phased Teacher Sampling.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation SG-OPD: Sign-Gated On-Policy Distillation via Sign-Consistency Gating and Phased Teacher Sampling

Reference 13

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source=pdf_text observed=2026-08-16T00:35:19.120078Z digest=sha256:c7ee96d732abc83c9e25fea723bb9b1fb9f806005630fa5765c5f7b0bfe74497

Observation 343ca932-becd-49a3-bfbd-a32217317fcd · outbound

This paper cites Reward-Gated On-Policy Distillation.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Reward-Gated On-Policy Distillation

Reference 14

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source=pdf_text observed=2026-08-16T00:35:19.124618Z digest=sha256:828ae16147b18b271f2dc087f8a1448436c5c2ea3ff86d2f7b132a482083979d

Observation 60ab21a5-297c-438c-b8c9-98d94c2130db · outbound

This paper cites Proximal Policy Optimization Algorithms.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Proximal Policy Optimization Algorithms

Reference 15

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source=pdf_text observed=2026-08-16T00:35:19.129403Z digest=sha256:51f41c3a45d387d5f7982a66c541db9a5f695456c3f65807654124077c0992b8

Observation 2cc351a7-de7f-42b0-8719-30b5195aac97 · outbound

This paper cites Qwen3 Technical Report.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Qwen3 Technical Report

Reference 16

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source=pdf_text observed=2026-08-16T00:35:19.134187Z digest=sha256:309927d692afc4a8e93bdef439d542a0c9b1ef781bf6cba2088d74ec953d2d37

Observation d455d430-d7af-4006-9d85-f977df22ef75 · outbound

This paper cites DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning

Reference 17

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source=pdf_text observed=2026-08-16T00:35:19.138789Z digest=sha256:4597f7ca940f4ca2b3c88bab2cad5a611d5ac11a16696b0dbade408273c2dd25

Observation bd8c6ada-e011-45c3-ac60-530c11e2b87b · outbound

This paper cites Advancing LLM Reasoning Generalists with Preference Trees.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Advancing LLM Reasoning Generalists with Preference Trees

Reference 18

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source=pdf_text observed=2026-08-16T00:35:19.143448Z digest=sha256:60d7981e3ed0707a043e171bf9aaa51c076b957d63ad55c566fc89fa69c492dc

Observation 4df29590-f01d-4cb7-982a-e9b5594f2c56 · outbound

This paper cites Decoupled weight de- cay regularization.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Decoupled weight de- cay regularization

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T00:35:19.148303Z digest=sha256:2e0c6130f03f9c3b1cc8f91601c04901e1a5b44f8ee4dc7396047e260ffc230e

Observation 7b3b7e15-b9d8-4d91-a529-f2e623f53190 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Evaluating Large Language Models Trained on Code

Reference 20

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source=pdf_text observed=2026-08-16T00:35:19.152756Z digest=sha256:9811d33eed386d625e46d6f0417a3ec445021c154be2eff23065b0e495a04bfe

Observation 1ec700ea-8cb8-495b-8eb0-b14d17c86318 · outbound

This paper cites Program Synthesis with Large Language Models.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Program Synthesis with Large Language Models

Reference 21

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source=pdf_text observed=2026-08-16T00:35:19.157784Z digest=sha256:aad81530dc9835d4563e6453ab7ed35ecfca50e36f6f606a720c36cec9c61a3b

Observation 0e08ed39-0c87-412e-9dba-2d8a1f67fe99 · outbound

This paper cites Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation

Reference 22

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source=pdf_text observed=2026-08-16T00:35:19.162472Z digest=sha256:09f45c47100c38326af41a73bbb676dd12d76615b1f93d545d1ecc1e1f287376

Observation a93e8f58-8d3a-493b-a754-e4b59ae2cf4b · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 23

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source=pdf_text observed=2026-08-16T00:35:19.167249Z digest=sha256:982f6948941ee9252e950dde618cf85f8c9273d40c2e758fcb4400b3d7b00b88

Observation ce6c5471-a501-4538-a6d5-e41177e99e63 · outbound

This paper cites TIP: Token Importance in On-Policy Distillation.

REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation TIP: Token Importance in On-Policy Distillation

Reference 2026

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source=pdf_text observed=2026-08-16T00:35:19.070631Z digest=sha256:e9acb02cb22d883fa45776c9e1d6f9cc6ea0b64fad7f6116f59e83c3159c0f00

Pith citing papers

No inbound Pith citation observations are available.