Paper Citation Record · LEDGER

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization

As of 12 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2412.03822.

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

pith.paper-citation-record.v1
2412.03822 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:06:51.356046Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T01:32:04.660400Z

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

57 of 57 outbound references displayed

verified exact0
verified fuzzy12
unresolved44
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 99e1cee2-2870-4e1b-8b6b-e677a898dcd2 · outbound

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

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Training language models to follow instructions with human feedback

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.044240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfa3e659-91c5-40bb-8b5b-5b9a6682aea9 · outbound

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

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.050083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db57cba3-db7b-466e-b44f-8a45c1b5ce2f · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.056099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d6f3939-1a8e-41c5-941e-743146953699 · outbound

This paper cites GPT-4 Technical Report.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization GPT-4 Technical Report

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.061791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22760f65-963c-4a7c-98d9-e29b8cac525e · outbound

This paper cites How Far Are We From AGI: Are LLMs All We Need?.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization How Far Are We From AGI: Are LLMs All We Need?

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.067338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97a45ed3-b108-43d3-b22c-bbd68b0d9935 · outbound

This paper cites Learning to summarize with human feedback.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Learning to summarize with human feedback

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.073149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2752897-a48e-4fcd-8676-bd76aaf0634e · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Direct preference optimization: Your language model is secretly a reward model

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.079176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 802c7432-bfa4-4aa4-bf4b-795b6f5085a5 · outbound

This paper cites SimPO: Simple Preference Optimization with a Reference-Free Reward.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization SimPO: Simple Preference Optimization with a Reference-Free Reward

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.083942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21b18e6a-5855-4e9d-8e84-92dcd08b8b93 · outbound

This paper cites The History and Risks of Reinforcement Learning and Human Feedback.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization The History and Risks of Reinforcement Learning and Human Feedback

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.089208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b79f6e8f-9af0-405d-870f-bf3606118fb6 · outbound

This paper cites The past, present and better future of feedback learning in large language models for subjective human preferences and values.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization The past, present and better future of feedback learning in large language models for subjective human preferences and values

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:06:52.377687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 388ff225-b08c-4b3a-8bcf-c0d955670635 · outbound

This paper cites Artificial Intelligence, Values and Alignment.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Artificial Intelligence, Values and Alignment

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.099490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 028d699f-485d-4526-867a-7036adcfa59f · outbound

This paper cites Artificial Intelligence, Humanistic Ethics.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Artificial Intelligence, Humanistic Ethics

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:06:52.359427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 721b89fe-31a3-4c83-a215-fb417b6944ce · outbound

This paper cites Beyond Preferences in AI Alignment.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Beyond Preferences in AI Alignment

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.116195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80e5b85b-db90-48d5-af76-5386352a2606 · outbound

This paper cites Meta Community Forum: Re- sults Analysis.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Meta Community Forum: Re- sults Analysis

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:06:52.341743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e15e84d2-04ba-4681-b23e-f62263bc02dc · outbound

This paper cites STELA: a community-centred approach to norm elicitation for AI alignment.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization STELA: a community-centred approach to norm elicitation for AI alignment

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.125962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59936292-d71c-4b0e-b898-45c3c8c5b2e2 · outbound

This paper cites Constitutional AI: Harmlessness from AI Feedback.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Constitutional AI: Harmlessness from AI Feedback

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.130971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db993abc-2c68-4cd6-8ce3-5b35f2fc6b72 · outbound

This paper cites Improving alignment of dialogue agents via targeted human judgements.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Improving alignment of dialogue agents via targeted human judgements

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.136182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99f768e8-7b04-482e-aa2b-8ad5b81e2964 · outbound

This paper cites Collective constitutional ai: Aligning a language model with public input.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Collective constitutional ai: Aligning a language model with public input

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.141942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 433669fe-1be5-4bf8-8e0f-5eb8d9ca4342 · outbound

This paper cites Introducing Meta Llama 3: The most capable openly available LLM to date, April.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Introducing Meta Llama 3: The most capable openly available LLM to date, April

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:06:52.311965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 14a7b8f0-b84a-4342-aa2d-8adb9e2c6fb8 · outbound

This paper cites MMToM-QA: Multimodal Theory of Mind Question Answering.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization MMToM-QA: Multimodal Theory of Mind Question Answering

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.157581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdf7d294-b607-472a-bee8-ccc13d0ce288 · outbound

This paper cites ChatGPT’s weekly users have doubled in less than a year.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization ChatGPT’s weekly users have doubled in less than a year

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:06:52.277815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3bf9c8eb-cb4a-4d79-9527-d85eb53a5e0e · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization LaMDA: Language Models for Dialog Applications

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.167430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afd6c360-f8af-41b0-aee8-fa567431ebfd · outbound

This paper cites DICES Dataset: Diversity in Conversational AI Evaluation for Safety.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization DICES Dataset: Diversity in Conversational AI Evaluation for Safety

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.172816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77e596d9-6cbc-4ec4-8aa7-bcae2448e9e5 · outbound

This paper cites Pretraining language models with human preferences.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Pretraining language models with human preferences

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.177999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a78eb4c6-df25-4f00-80ef-efc87adc010b · outbound

This paper cites alignment.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization alignment

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:06:52.249075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8686eea0-48ae-458c-86a8-cf66d1761691 · outbound

This paper cites Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.187916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e117d74-1c97-478c-af8f-2e7605ad7c3f · outbound

This paper cites The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.192853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a28e7541-daf6-4fef-8bc8-079c0e99f380 · outbound

This paper cites The Cultural Psychology of Large Language Models: Is ChatGPT a Holistic or Analytic Thinker?.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization The Cultural Psychology of Large Language Models: Is ChatGPT a Holistic or Analytic Thinker?

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.198392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da0a89d7-7cf0-4605-8415-302fdd4ef571 · outbound

This paper cites Diverging preferences: When do annotators disagree and do models know? arXiv preprint arXiv:2410.14632, 2024.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Diverging preferences: When do annotators disagree and do models know? arXiv preprint arXiv:2410.14632, 2024

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.203705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 381a39c0-e688-4706-86aa-ce211ba081a8 · outbound

This paper cites Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.209034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f071d434-42d9-4bc0-85d9-6610397831de · outbound

This paper cites Personalized Language Modeling from Personalized Human Feedback.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Personalized Language Modeling from Personalized Human Feedback

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.214914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fed5acb-3a7c-47bd-853b-55d90103f283 · outbound

This paper cites Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.220449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b78df44-c63b-4647-8235-1d4c0525780a · outbound

This paper cites Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards.Advances in Neural Information Processing Systems, 36, 2024.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards.Advances in Neural Information Processing Systems, 36, 2024

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:06:52.232281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ecf77a7b-1b82-40c2-9009-138632ef1698 · outbound

This paper cites Fine-tuning language models to find agreement among humans with diverse preferences.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Fine-tuning language models to find agreement among humans with diverse preferences

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.230994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d01beb3-7a78-4607-acee-3dd57276c533 · outbound

This paper cites MaxMin-RLHF: Alignment with Diverse Human Preferences.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization MaxMin-RLHF: Alignment with Diverse Human Preferences

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.235932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 340b4c90-159c-45f6-87e5-8086a3184a9c · outbound

This paper cites Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.241253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23783648-074d-49db-887b-bd1fdb950640 · outbound

This paper cites Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.247150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08fcd4e1-cc17-4c7d-a100-2797d16e8a90 · outbound

This paper cites Aligning Crowd Feedback via Distributional Preference Reward Modeling.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Aligning Crowd Feedback via Distributional Preference Reward Modeling

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.252782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76c581c7-7fd2-4bd6-9bfb-61de741da16d · outbound

This paper cites Uncertainty-aware Reward Model: Teaching Reward Models to Know What is Unknown.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Uncertainty-aware Reward Model: Teaching Reward Models to Know What is Unknown

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.258372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc4ed378-108a-4b3d-b1da-7c481dec6011 · outbound

This paper cites WebGPT: Browser-assisted question-answering with human feedback.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization WebGPT: Browser-assisted question-answering with human feedback

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.264538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27d43019-d72c-437d-bd35-d10a9dc4b073 · outbound

This paper cites synthetic-instruct-gptj-pairwise (revision cc92d8d),.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization synthetic-instruct-gptj-pairwise (revision cc92d8d),

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:06:52.203555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2ade0842-68ab-4012-b155-8a9a76cbb5d4 · outbound

This paper cites Ultrafeedback: Boosting language models with high-quality feedback, 2023.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Ultrafeedback: Boosting language models with high-quality feedback, 2023

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.285345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06542e24-3659-4559-ab7f-0bd9add0c4b6 · outbound

This paper cites The Llama 3 Herd of Models.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization The Llama 3 Herd of Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.291078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac75e5fa-82e2-4ce3-ae80-ffa97cef70bc · outbound

This paper cites Transformers: State- of-the-art natural language processing.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Transformers: State- of-the-art natural language processing

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:06:52.156056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 396228b2-8607-415e-a327-5bec2dd767af · outbound

This paper cites Holistic Evaluation of Language Models.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Holistic Evaluation of Language Models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.303089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7bab6bc-55b5-4a19-87c5-021f937b027b · outbound

This paper cites Argyle, Ethan C.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Argyle, Ethan C

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:06:52.137895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 06bdd5c2-9118-4f2d-9ee2-a7dbd851d771 · outbound

This paper cites Are large language models good annotators? In Proceedings on, pages 38–48.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Are large language models good annotators? In Proceedings on, pages 38–48

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:06:52.120199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ff1e3223-68a9-40a2-bd97-2f351a502601 · outbound

This paper cites Automated social science: Language models as scientist and subjects.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Automated social science: Language models as scientist and subjects

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.327536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bf040e1-683c-4a15-8c3e-9923cb3bf3b1 · outbound

This paper cites Large language models that replace human participants can harmfully misportray and flatten identity groups.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Large language models that replace human participants can harmfully misportray and flatten identity groups

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.333381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1068a39-73d5-4815-8761-172725cef2d9 · outbound

This paper cites Out of One, Many: Using Language Models to Simulate Human Samples.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Out of One, Many: Using Language Models to Simulate Human Samples

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.315201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6da6ab26-ac28-4126-945e-024afc033184 · outbound

This paper cites Stevie Bergman, Jennifer Chien, Mark Díaz, Seliem El-Sayed, Jaylen Pittman, Shakir Mohamed, and Kevin R.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Stevie Bergman, Jennifer Chien, Mark Díaz, Seliem El-Sayed, Jaylen Pittman, Shakir Mohamed, and Kevin R

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:06:52.077496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 39f114fe-2bd6-4ba9-9612-161e00d9ce6a · outbound

This paper cites A Roadmap to Pluralistic Alignment.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization A Roadmap to Pluralistic Alignment

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.356046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3760899-b7e0-433e-b121-187ef371e613 · outbound

This paper cites Whose opinions do language models reflect? In International Conference on Machine Learning, pages 29971–30004.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Whose opinions do language models reflect? In International Conference on Machine Learning, pages 29971–30004

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:51.339375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91f673de-6b3d-4e78-b56a-ffc829469bca · outbound

This paper cites The illusion of artificial inclusion.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization The illusion of artificial inclusion

Reference 56

Resolution: metadata mismatch; local_arxiv, observed 2026-08-11T22:06:51.462091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8d6231ee-7d13-4e0b-b006-28e9bb0d1b04 · outbound

This paper cites doi: 10.1162/daed_a_01912.

Beyond the Binary: Capturing Diverse Preferences With Reward Regularization doi: 10.1162/daed_a_01912

Reference 2022

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Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Unresolved cited work

Reference 2023

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Beyond the Binary: Capturing Diverse Preferences With Reward Regularization Unresolved cited work

Reference 2024

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What Do People Actually Want From AI? Mapping Preference Plurality cites this paper.

What Do People Actually Want From AI? Mapping Preference Plurality Beyond the Binary: Capturing Diverse Preferences With Reward Regularization

Reference 67

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