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Paper Citation Record · LEDGER

Direct Preference Knowledge Distillation for Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2406.19774.

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

pith.paper-citation-record.v1
2406.19774 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:27:49.311523Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5fbb818f-063b-466e-85ef-e368a5800469 · inbound

Online Knowledge Distillation with Reward Guidance cites this paper.

Online Knowledge Distillation with Reward Guidance Direct Preference Knowledge Distillation for Large Language Models

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T14:27:49.311523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:27:49.311523Z digest=sha256:6019d30a7469a1cd4df206f96d700c22413e60a20e2a2767e298dd242ab1ddd4

Observation cbf0e1d1-9422-455e-89cd-b2c3611ec0ae · inbound

daDPO: Distribution-Aware DPO for Distilling Conversational Abilities cites this paper.

daDPO: Distribution-Aware DPO for Distilling Conversational Abilities Direct Preference Knowledge Distillation for Large Language Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T11:31:46.108909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:31:46.108909Z digest=sha256:b80833cd04e012ee0ee6fa3bd12c4c8bd03b872d50599d8428012d494725f828

Observation 9f0bc812-d474-4d61-bbfa-f4c4e9543a29 · inbound

Not All Preferences are What You Need for Post-Training: Selective Alignment Strategy for Preference Optimization cites this paper.

Not All Preferences are What You Need for Post-Training: Selective Alignment Strategy for Preference Optimization Direct Preference Knowledge Distillation for Large Language Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T18:38:09.954900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:38:09.954900Z digest=sha256:271e62a0f1df3b8b4972917a811a2bc088e70f0edaa17b6ce9891f4a4d6487a3

Observation d31023e5-7897-444b-a6ca-3cbc8878500c · inbound

DPO Unchained: Your Training Algorithm is Secretly Disentangled in Human Choice Theory (and its Loss' Convexity is Dispensable) cites this paper.

DPO Unchained: Your Training Algorithm is Secretly Disentangled in Human Choice Theory (and its Loss' Convexity is Dispensable) Direct Preference Knowledge Distillation for Large Language Models

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-06T18:52:36.169261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:52:36.169261Z digest=sha256:ceab59ed9402672579800cbbddd4f8dadf2390c64a38dcb72ad39b4b8b6cd5cf

Observation 4f22e24f-e5c6-414b-ab97-9ac9f30be085 · inbound

Dynamics of Learning under User Choice: Overspecialization and Peer-Model Probing cites this paper.

Dynamics of Learning under User Choice: Overspecialization and Peer-Model Probing Direct Preference Knowledge Distillation for Large Language Models

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-02T20:24:30.971052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T20:24:30.971052Z digest=sha256:8e0639132b65c37b14cf744f898712ef8500b202d678f77ae60756b70f160c70

Observation 60baec07-81e0-4715-b37d-6ab6c6af7ea0 · inbound

ExecTune: Effective Steering of Black-Box LLMs with Guide Models cites this paper.

ExecTune: Effective Steering of Black-Box LLMs with Guide Models Direct Preference Knowledge Distillation for Large Language Models

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-05-11T07:55:58.672118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T16:55:34.091812Z digest=sha256:44b17d3f65098271084d71f367ca69c6f629e187efe43eb5ef849114ff28cdfd

Observation d87bd08b-3725-4d2c-a8a8-141c09aa6a51 · inbound

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers cites this paper.

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers Direct Preference Knowledge Distillation for Large Language Models

Reference 118

Resolution
verified exact
arxiv_id, observed 2026-06-28T23:32:46.720349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-28T23:29:02.457697Z digest=sha256:ea6b9cb09cc04518dbcc8fca4de91c0abd6492f38db889cd017db6cc6997202b

Observation a62cec2c-5f63-478d-8ccf-34e53e1695cd · inbound

Understanding Knowledge Distillation in Post-Training: When It Helps and When It Fails cites this paper.

Understanding Knowledge Distillation in Post-Training: When It Helps and When It Fails Direct Preference Knowledge Distillation for Large Language Models

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-06-26T08:39:15.122401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-26T08:31:47.633155Z digest=sha256:23f9cdfa1c0c11109aa73b6f41fddc89381183e96063c2ec8401f609e482fd96

Observation ee6be6f7-8aeb-45d6-83dd-b39a70fba64e · inbound

ARKD: Adaptive Reinforcement Learning-Guided Bidirectional KL Divergence Distillation for Text Generation cites this paper.

ARKD: Adaptive Reinforcement Learning-Guided Bidirectional KL Divergence Distillation for Text Generation Direct Preference Knowledge Distillation for Large Language Models

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-06-30T06:04:21.662373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-30T05:59:13.483829Z digest=sha256:b7cce376ff1262ff67cbf0ad6a021030c5230ae352fb9de052e50f163f60b69c