Paper Citation Record · LEDGER

Learn Your Reference Model for Real Good Alignment

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2404.09656.

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

pith.paper-citation-record.v1
2404.09656 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:40:17.894671Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T03:45:17.579700Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5adf27ae-e16d-4764-acd0-451d9711114c · inbound

Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization cites this paper.

Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization Learn Your Reference Model for Real Good Alignment

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:16:17.362296Z

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 fe31d83a-e06a-440a-8617-2220db8220f1 · inbound

The Differences Between Direct Alignment Algorithms are a Blur cites this paper.

The Differences Between Direct Alignment Algorithms are a Blur Learn Your Reference Model for Real Good Alignment

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:52:29.429125Z

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 9bf454c0-d0c9-470b-99ac-b9b245bc4f18 · inbound

Explicit Preference Optimization: No Need for an Implicit Reward Model cites this paper.

Explicit Preference Optimization: No Need for an Implicit Reward Model Learn Your Reference Model for Real Good Alignment

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T05:40:17.894671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cad5854e-c103-4d4f-8d73-ebc7127bc9aa · inbound

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory cites this paper.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory Learn Your Reference Model for Real Good Alignment

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-07T01:04:30.926721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baeca0d5-9899-458f-8bbf-98d5bbf932e0 · inbound

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution Learn Your Reference Model for Real Good Alignment

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:37:28.523968Z

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 d6a6a91b-0d07-4461-819f-b2bb0e42711a · inbound

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution Learn Your Reference Model for Real Good Alignment

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-21T13:10:10.461804Z

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 8e0fb4a1-3870-4b13-9907-f1cfd2811068 · inbound

Intrinsic Mutual Information as a Modulator for Preference Optimization cites this paper.

Intrinsic Mutual Information as a Modulator for Preference Optimization Learn Your Reference Model for Real Good Alignment

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:41:19.142242Z

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 7cae48a0-bef3-4259-b815-4b029bd91f98 · inbound

TPMM-DPO: Trajectory-aware Preference-guided Model Merging for Iterative Direct Preference Optimization cites this paper.

TPMM-DPO: Trajectory-aware Preference-guided Model Merging for Iterative Direct Preference Optimization Learn Your Reference Model for Real Good Alignment

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:45:17.582152Z

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.