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Paper Citation Record · LEDGER

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO

As of 15 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 3 inbound Pith citation observations for arXiv:2505.15694.

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

pith.paper-citation-record.v1
2505.15694 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:19:09.839089Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:45:05.223275Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T20:13:13.616657Z

Reference resolution

27 of 27 outbound references displayed

  • verified exact3
  • verified fuzzy9
  • unresolved14
  • parse uncertain0
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External citation measurements

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Outbound references

Observation cf07d1dc-5adf-4e5b-92bb-781c88a6afce · outbound

This paper cites Claim E.4.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Claim E.4

Reference 1

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Observation 4f432d9e-58b8-45dc-9942-58deb2a88a92 · outbound

This paper cites Thus, by Lemma H.2, we have with probability at least 1 − δ, 1 n nX i=1 ηixi 2 ≤ C · σ · r 1 + ln(1/δ) n , for some universal constant C >0.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Thus, by Lemma H.2, we have with probability at least 1 − δ, 1 n nX i=1 ηixi 2 ≤ C · σ · r 1 + ln(1/δ) n , for some universal constant C >0

Reference 2

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Observation 8ac5c9d9-abc4-42c5-9969-35103f8b9218 · outbound

This paper cites Finally, we use the win rate from these comparisons as our primary performance metric, following the methodology outlined in the DPO paper (Rafailov et al., 2023).

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Finally, we use the win rate from these comparisons as our primary performance metric, following the methodology outlined in the DPO paper (Rafailov et al., 2023)

Reference 5

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Observation 8aa47b56-c5b2-4a2c-ab47-3ca7a5fa914b · outbound

This paper cites Manipulation attacks in local differential privacy.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Manipulation attacks in local differential privacy

Reference 7

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Observation eb00ceec-6840-4f45-91ae-7ba1ebbef8c7 · outbound

This paper cites Differentially Private Reward Estimation with Preference Feedback.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Differentially Private Reward Estimation with Preference Feedback

Reference 8

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Observation 6f6217b9-2b21-4366-b350-b0d52c2f1c40 · outbound

This paper cites Provably Robust DPO: Aligning Language Models with Noisy Feedback.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Provably Robust DPO: Aligning Language Models with Noisy Feedback

Reference 9

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Observation 03059725-2090-47c3-94f8-d5d389713b53 · outbound

This paper cites C., Jordan, M.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO C., Jordan, M

Reference 10

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Observation 4e27a73e-4b82-4766-831b-99727d532408 · outbound

This paper cites A tail inequality for quadratic forms of subgaussian random vectors.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO A tail inequality for quadratic forms of subgaussian random vectors

Reference 12

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Observation f79755dd-1d76-499c-877f-e45cedcb5d0d · outbound

This paper cites Corruption Robust Offline Reinforcement Learning with Human Feedback.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Corruption Robust Offline Reinforcement Learning with Human Feedback

Reference 14

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Observation d9904036-a23e-4ef2-b25c-ac1113470fd4 · outbound

This paper cites Dueling RL: Reinforcement Learning with Trajectory Preferences.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Dueling RL: Reinforcement Learning with Trajectory Preferences

Reference 15

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Observation 7187fcf9-6b79-4ced-b4d4-3c4c4aa06903 · outbound

This paper cites The importance of online data: Understanding prefer- ence fine-tuning via coverage.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO The importance of online data: Understanding prefer- ence fine-tuning via coverage

Reference 16

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Observation 4c1e7082-13de-40a7-a7a9-ffe05b95f5db · outbound

This paper cites Provable Offline Preference-Based Reinforcement Learning.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Provable Offline Preference-Based Reinforcement Learning

Reference 19

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Observation e6bcd209-91de-4231-b8dd-d47083cb7456 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Fine-Tuning Language Models from Human Preferences

Reference 20

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Observation 8342ec0c-c248-43a5-81ee-c13432b97416 · outbound

This paper cites Additional Related Work We discuss here more relevant work that do not fit in the main text.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Additional Related Work We discuss here more relevant work that do not fit in the main text

Reference 21

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 7cbe31bb-7cdd-491b-b353-54ac960f42ad · outbound

This paper cites an unresolved cited work.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Unresolved cited work

Reference 22

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation c7fd88bc-2068-45e5-8c2b-5d0552f1d9b5 · outbound

This paper cites rejected.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO rejected

Reference 23

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation a8e615f1-ea4c-4250-8402-d0df3edebdc4 · outbound

This paper cites Chosen” and “Rejected.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Chosen” and “Rejected

Reference 27

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 791c3347-9199-4ea2-90c8-b9eb91a052ad · outbound

This paper cites Robust Reinforcement Learning from Corrupted Human Feedback.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Robust Reinforcement Learning from Corrupted Human Feedback

Reference 1952

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local_arxiv, observed 2026-08-07T15:19:10.582859Z

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Observation 7b7c3549-e50f-4f13-b26a-6ea1eb994b06 · outbound

This paper cites Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF

Reference 1965

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source=pdf_text observed=2026-08-07T15:19:09.445181Z digest=sha256:c61420dfc8f5d35a5e9d8b5bc46ced8afdd16c187342e08e9388c9f9cf679fee

Observation 3e81486b-232c-4b7b-b93b-ba130a1d6d1d · outbound

This paper cites Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization

Reference 2011

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source=pdf_text observed=2026-08-07T15:19:09.183433Z digest=sha256:38438fd2dbc0b71a2cc387be1b6857440afc541b77f827fb63ae082e54b3010b

Observation e8c9eb8f-a6e9-4b2c-8181-a1e88602ff0c · outbound

This paper cites Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment

Reference 2014

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Observation f5aa4cf2-f486-4ab7-a49d-81279ea92799 · outbound

This paper cites Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability

Reference 2019

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Observation b539ee6f-cebb-4b93-93e9-627b6d143fdb · outbound

This paper cites Reinforcement Learning for LLM Post-Training: A Survey.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Reinforcement Learning for LLM Post-Training: A Survey

Reference 2020

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Observation 40ae400d-0164-464d-9486-e1460f501434 · outbound

This paper cites Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models

Reference 2021

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local_arxiv, observed 2026-08-07T15:19:10.757471Z

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source=pdf_text observed=2026-08-07T15:19:07.927499Z digest=sha256:68b16cb72fbb4a2c208f331e587c3788b3c11c36aac245b06dc44e90b960cf9a

Observation 63cf1714-d86f-4816-a113-9bd108e73088 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 2022

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source=pdf_text observed=2026-08-07T15:19:08.018639Z digest=sha256:3d2a192a73bd1896ff9141a7ec36d53eb2f776c7f386690fc06c0a64bc86ae1f

Observation 9c8f5672-e659-495c-8784-2e003c483aad · outbound

This paper cites Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF

Reference 2023

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Observation 9bf3d758-8c50-40ea-88e2-3edf4d488e02 · outbound

This paper cites Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 2024

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source=pdf_text observed=2026-08-07T15:19:08.255598Z digest=sha256:4264fc689c85d86750b8e8e7239f6fa1fcea67db93d32bd04440055df79a8a94

Pith citing papers

Observation f1837fc3-a321-403a-b96b-5aad2be5b011 · inbound

Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment cites this paper.

Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO

Reference 101

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source=arxiv_source observed=2026-08-07T13:45:05.223275Z digest=sha256:37643aa043366a48b028f7cee609d8aa4267a0a0178ea767e748e0a57dc3e85b

Observation cd1778d0-8b58-41b0-928e-ab3f08ae8001 · inbound

Reinforcement Learning from Human Feedback: A Statistical Perspective cites this paper.

Reinforcement Learning from Human Feedback: A Statistical Perspective A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO

Reference 95

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arxiv_id, observed 2026-05-13T20:13:13.620120Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-13T20:10:43.578904Z digest=sha256:b8271d8633ace5011b81568c172817ba2c368cdf09608f5236a9bdad006c83be

Observation 0157b7c1-6d1c-441f-87e2-2170d262c79c · inbound

Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training cites this paper.

Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training A Unified Theoretical Analysis of Private and Robust Offline Alignment: from RLHF to DPO

Reference 24

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arxiv_id, observed 2026-05-13T06:32:24.252477Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-13T06:30:51.812541Z digest=sha256:66cd51351b1b31f2c4dcb8e1a5b8107ca089ee5a9db98e2dbdfa4fa6243885ac