Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Direct Preference Knowledge Distillation for Large Language Models
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2406.19774.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:52:36.169261Z
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 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.
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.
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.
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 2026-08-07T06:34:17.273281+00:00.
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 2026-08-07T06:34:17.273281+00:00.
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 2026-08-07T06:34:17.273281+00:00.
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 2026-08-07T06:34:17.273281+00:00.