Paper Citation Record · LEDGER

Aligning LLMs with Domain Invariant Reward Models

As of 13 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2501.00911.

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

pith.paper-citation-record.v1
2501.00911 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:45:12.452310Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

verified exact2
verified fuzzy4
unresolved47
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ec3479bc-16d1-42ff-a4ff-c498c8b31ee3 · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:45:13.434617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4b844550-bd51-45df-8c11-613835eda028 · outbound

This paper cites https://claude.ai/ Claude.

Aligning LLMs with Domain Invariant Reward Models https://claude.ai/ Claude

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:13.417746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4a3f310d-bb93-4017-8622-985e59cf9656 · outbound

This paper cites Wasserstein GAN.

Aligning LLMs with Domain Invariant Reward Models Wasserstein GAN

Reference 3

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Observation 22a944a4-4d6f-4d3f-8f29-81a8644bf16b · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 4

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Observation 4a0b8ffb-f07f-4273-8905-0b6072b4c0e3 · outbound

This paper cites Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference.

Aligning LLMs with Domain Invariant Reward Models Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.187680Z

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Observation 9fd5e676-b502-475b-96b3-aa5f68fe30fd · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:45:13.390203Z

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Observation b63d2dc6-dc34-44d1-b8f6-9dc536e35640 · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:45:13.370858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f22e4843-f4f6-4675-a54d-bd31bcd59cb4 · outbound

This paper cites The Llama 3 Herd of Models.

Aligning LLMs with Domain Invariant Reward Models The Llama 3 Herd of Models

Reference 9

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Source-reported events for the cited work

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Observation 51e8f850-9bfc-4a31-b938-e890656a2175 · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.215495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3beec7c8-733f-454b-8434-ebf1fdfb028b · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:45:13.339535Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 80268a2a-c2ba-4c37-bdda-e06be7ef805e · outbound

This paper cites Ustinova, Hana Ajakan, Pascal Germain, H.

Aligning LLMs with Domain Invariant Reward Models Ustinova, Hana Ajakan, Pascal Germain, H

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:13.321934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0eff83a4-e1f8-4ef7-ba63-5a00b1299d15 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Aligning LLMs with Domain Invariant Reward Models Gemma 2: Improving Open Language Models at a Practical Size

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.230638Z

Source-reported events for the cited work

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Observation 2d8c36a4-6021-4a39-bfe8-28126657e856 · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Aligning LLMs with Domain Invariant Reward Models Gemma: Open Models Based on Gemini Research and Technology

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.235972Z

Source-reported events for the cited work

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Observation 7d03db23-832b-4021-977d-f7bf771db709 · outbound

This paper cites Improved Training of Wasserstein GANs.

Aligning LLMs with Domain Invariant Reward Models Improved Training of Wasserstein GANs

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.241129Z

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Observation 525eec75-07ad-4631-9a9f-da21d0e2f561 · outbound

This paper cites Direct Language Model Alignment from Online AI Feedback.

Aligning LLMs with Domain Invariant Reward Models Direct Language Model Alignment from Online AI Feedback

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.246781Z

Source-reported events for the cited work

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Observation 9715775d-83cb-40a2-8121-0d37b211048c · outbound

This paper cites The Unreasonable Effectiveness of Easy Training Data for Hard Tasks.

Aligning LLMs with Domain Invariant Reward Models The Unreasonable Effectiveness of Easy Training Data for Hard Tasks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.252155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0482d9d0-e854-4409-83b1-de60c72f6608 · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:45:13.302982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1b571a44-facb-4844-b079-229b3f4fc963 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Aligning LLMs with Domain Invariant Reward Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.262601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c2111e6-485b-465c-b7c6-982e37b8167e · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:45:13.285434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f719bc80-b061-4a16-9be3-dd5ca1ac8290 · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:45:13.268035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5aa80a67-2fb3-40b9-af20-44ae63ebf13b · outbound

This paper cites UDALM: Unsupervised Domain Adaptation through Language Modeling.

Aligning LLMs with Domain Invariant Reward Models UDALM: Unsupervised Domain Adaptation through Language Modeling

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:45:12.897397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f121deb6-27f1-4e45-a3cb-39abe0b41336 · outbound

This paper cites Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment.

Aligning LLMs with Domain Invariant Reward Models Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.283131Z

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Unavailable: canonical work link unavailable.

Observation 399c1371-c5ea-41d9-900a-eb1a7ec50e34 · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:45:13.247219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cac133d6-8dd6-4c6a-8397-d1f010ce696d · outbound

This paper cites Understanding the Effects of RLHF on LLM Generalisation and Diversity.

Aligning LLMs with Domain Invariant Reward Models Understanding the Effects of RLHF on LLM Generalisation and Diversity

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.293458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f67971a3-3e53-47bb-bb5f-339ef22d38be · outbound

This paper cites RewardBench: Evaluating Reward Models for Language Modeling.

Aligning LLMs with Domain Invariant Reward Models RewardBench: Evaluating Reward Models for Language Modeling

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.299196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2855cfac-44af-460d-9951-9d95e4373c8c · outbound

This paper cites Scalable agent alignment via reward modeling: a research direction.

Aligning LLMs with Domain Invariant Reward Models Scalable agent alignment via reward modeling: a research direction

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.304282Z

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Observation ad49babe-b993-4957-9fee-352854386105 · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:45:13.228994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation adb34e46-df8e-40f3-9fda-79761d91fc62 · outbound

This paper cites Preference Tuning For Toxicity Mitigation Generalizes Across Languages.

Aligning LLMs with Domain Invariant Reward Models Preference Tuning For Toxicity Mitigation Generalizes Across Languages

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.315612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73d1d84f-8ece-4bb0-8598-380f9625dd49 · outbound

This paper cites Decoupled Weight Decay Regularization.

Aligning LLMs with Domain Invariant Reward Models Decoupled Weight Decay Regularization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.322212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df829618-1316-49a3-ba2b-111a0c5aa761 · outbound

This paper cites No Language Left Behind: Scaling Human-Centered Machine Translation.

Aligning LLMs with Domain Invariant Reward Models No Language Left Behind: Scaling Human-Centered Machine Translation

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.326988Z

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Observation c9293c1f-65b5-46e0-81f3-3e1e4d1441dd · outbound

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Aligning LLMs with Domain Invariant Reward Models https://chatgpt.com/ Chatgpt

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:13.211159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation aad28e62-e728-4eea-bb76-3be7c1245df8 · outbound

This paper cites Training language models to follow instructions with human feedback.

Aligning LLMs with Domain Invariant Reward Models Training language models to follow instructions with human feedback

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.338619Z

Source-reported events for the cited work

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Observation 8eb05ed5-6849-4bc4-ad4b-7687b0f1e684 · outbound

This paper cites XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models.

Aligning LLMs with Domain Invariant Reward Models XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.344286Z

Source-reported events for the cited work

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Observation b2c82572-4120-4f94-971a-a25ff1d87f10 · outbound

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Aligning LLMs with Domain Invariant Reward Models Proximal Policy Optimization Algorithms

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.349688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c68407d2-bd69-4615-b5b9-025746aa746f · outbound

This paper cites Aligning Language Models with Demonstrated Feedback.

Aligning LLMs with Domain Invariant Reward Models Aligning Language Models with Demonstrated Feedback

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.355146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb8d7799-1e80-43f4-8d2a-29b83943b9e6 · outbound

This paper cites Wasserstein Distance Guided Representation Learning for Domain Adaptation.

Aligning LLMs with Domain Invariant Reward Models Wasserstein Distance Guided Representation Learning for Domain Adaptation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.361165Z

Source-reported events for the cited work

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Observation 9d92ae9d-cbe4-41d3-96c7-d9cd1c25a657 · outbound

This paper cites Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision.

Aligning LLMs with Domain Invariant Reward Models Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.367288Z

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Unavailable: canonical work link unavailable.

Observation 2de6c780-9bdd-4dee-835c-7e2d8618b558 · outbound

This paper cites Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment.

Aligning LLMs with Domain Invariant Reward Models Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.372847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef303f7c-fb2a-4fb1-8fde-c11ed3a7cf1d · outbound

This paper cites Causal Confusion and Reward Misidentification in Preference-Based Reward Learning.

Aligning LLMs with Domain Invariant Reward Models Causal Confusion and Reward Misidentification in Preference-Based Reward Learning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.378181Z

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Unavailable: canonical work link unavailable.

Observation 15eaf064-48e0-4a6e-8d5e-c6c2f796e857 · outbound

This paper cites Chernova, and Dhruv Batra.

Aligning LLMs with Domain Invariant Reward Models Chernova, and Dhruv Batra

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:45:13.181683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d9e7e8de-fa6f-4e8d-a26d-4cccde6dc901 · outbound

This paper cites Deep Domain Confusion: Maximizing for Domain Invariance.

Aligning LLMs with Domain Invariant Reward Models Deep Domain Confusion: Maximizing for Domain Invariance

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.388655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e561a12-24a5-4b02-b30d-94e811486f54 · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.393710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6adc45c0-0464-4e51-a507-fb1f30304374 · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:45:13.161418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a05d9238-5b9f-42d9-b0e6-ff3ebf2d2d76 · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:45:13.141030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1340b498-31a0-45e7-b283-1ec86018401b · outbound

This paper cites Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual Alignment.

Aligning LLMs with Domain Invariant Reward Models Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual Alignment

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:45:12.593278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 97b509e2-18b3-48b6-9e74-8dfa8fc741ee · outbound

This paper cites CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility.

Aligning LLMs with Domain Invariant Reward Models CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.414979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99232992-40d2-465d-94e1-27471d260e93 · outbound

This paper cites Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs.

Aligning LLMs with Domain Invariant Reward Models Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.420151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e724335a-a9ae-42b5-9421-b8240cf682a8 · outbound

This paper cites Generative Verifiers: Reward Modeling as Next-Token Prediction.

Aligning LLMs with Domain Invariant Reward Models Generative Verifiers: Reward Modeling as Next-Token Prediction

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.425370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0496f569-b2bf-4586-9b6e-075691c6365c · outbound

This paper cites Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

Aligning LLMs with Domain Invariant Reward Models Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.431407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a406a80-2238-4f13-bd0a-8ba6f59e3246 · outbound

This paper cites an unresolved cited work.

Aligning LLMs with Domain Invariant Reward Models Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:45:13.123721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f39061c4-0a81-46a2-bb04-17b0c58d1b40 · outbound

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

Aligning LLMs with Domain Invariant Reward Models Fine-Tuning Language Models from Human Preferences

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.441631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b527aeef-d4b6-463d-9df6-3949ecd8a411 · outbound

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Aligning LLMs with Domain Invariant Reward Models online" 'onlinestring :=

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.446752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1adb606a-3623-4c4a-a5c1-dfb6f422aee9 · outbound

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Aligning LLMs with Domain Invariant Reward Models write newline

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T22:45:12.452310Z

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Pith citing papers

No inbound Pith citation observations are available.