Paper Citation Record · LEDGER

Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2406.09279.

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

pith.paper-citation-record.v1
2406.09279 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:16:16.819602Z

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

3
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 fe69fe34-0db8-4220-8713-8b6916409610 · inbound

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model cites this paper.

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback

Reference 182

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:30:02.969419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7e660493-93ab-42cd-a656-900de269cc29 · inbound

Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective cites this paper.

Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T12:16:16.819602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea815533-e427-4c7e-b099-20514c7aea49 · inbound

AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs cites this paper.

AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T12:08:29.551925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dec2d96c-b540-40d5-b2fd-6ac2c13d463b · inbound

Rethinking DPO: The Role of Rejected Responses in Preference Misalignment cites this paper.

Rethinking DPO: The Role of Rejected Responses in Preference Misalignment Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T00:52:38.089108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 028f2908-3e8d-4bfa-a97f-5e506b9f1667 · inbound

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities cites this paper.

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T16:34:25.048128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0b7d5e2-33de-489d-9f63-86ba811a6a3c · inbound

Technical Report of TeleChat2, TeleChat2.5 and T1 cites this paper.

Technical Report of TeleChat2, TeleChat2.5 and T1 Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T14:43:22.272855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af047bad-18ff-499a-a601-9678bb2bd54c · inbound

Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality cites this paper.

Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-05T15:38:44.895600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a882cb0-f77d-49a1-89a6-40eadf2bf368 · inbound

Diversity in Large Language Models under Supervised Fine-Tuning cites this paper.

Diversity in Large Language Models under Supervised Fine-Tuning Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-09T20:37:32.172382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bf82f9d8-9dbe-47c0-8da7-6fb0ce028d69 · inbound

Diversity in Large Language Models under Supervised Fine-Tuning cites this paper.

Diversity in Large Language Models under Supervised Fine-Tuning Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:11:18.092443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f4269c14-d1a7-41e4-ada1-f407f23400bf · inbound

Leveraging Pretrained Language Models as Energy Functions for Glauber Dynamics Text Diffusion cites this paper.

Leveraging Pretrained Language Models as Energy Functions for Glauber Dynamics Text Diffusion Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:55:40.691816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.