Paper Citation Record · LEDGER

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation

As of 12 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 2 inbound Pith citation observations for arXiv:2411.14871.

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

pith.paper-citation-record.v1
2411.14871 v3

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:54:02.564230Z

measured 66 of 66 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T18:18:46.456767Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T18:23:50.834907Z

Reference resolution

64 of 64 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 22725af1-acb7-4d2d-a6fd-c89201d470d2 · outbound

This paper cites Direct preference optimization with an offset.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Direct preference optimization with an offset

Reference 1

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Observation 758c0570-25e7-4605-b00b-594ec78088a3 · outbound

This paper cites A general theoretical paradigm to understand learning from human prefer- ences.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation A general theoretical paradigm to understand learning from human prefer- ences

Reference 2

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Observation 782e039f-5c30-418e-8e6a-d57dbd4ec1ab · outbound

This paper cites Unified Preference Optimization: Language Model Alignment Beyond the Preference Frontier.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Unified Preference Optimization: Language Model Alignment Beyond the Preference Frontier

Reference 3

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Observation 2b999349-b618-4dcf-aa84-00c42f6e70ad · outbound

This paper cites Training diffusion models with reinforcement learning.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Training diffusion models with reinforcement learning

Reference 4

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Observation a2d26ae7-6a78-43e0-9af1-c9c96ac649ba · outbound

This paper cites Align your latents: High-resolution video syn- thesis with latent diffusion models.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Align your latents: High-resolution video syn- thesis with latent diffusion models

Reference 5

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Observation b024a3bf-2de2-4a2f-a352-6998ee14252e · outbound

This paper cites Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:54:03.316856Z

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Observation 977027d9-7eb1-4016-957b-b518703b7e01 · outbound

This paper cites Self-play fine-tuning converts weak language models to strong language models.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Self-play fine-tuning converts weak language models to strong language models

Reference 7

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Observation bac95065-43eb-497d-8324-21ca538c3fcd · outbound

This paper cites Christiano, Jan Leike, Tom B.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Christiano, Jan Leike, Tom B

Reference 8

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Observation 0ec493cb-2a78-4ba7-b681-a34471d88dec · outbound

This paper cites Diffu- sion models beat gans on image synthesis.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Diffu- sion models beat gans on image synthesis

Reference 9

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Observation f928534c-1c43-4ec2-864a-e6c50512e18e · outbound

This paper cites Scaling rectified flow transformers for high- resolution image synthesis.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Scaling rectified flow transformers for high- resolution image synthesis

Reference 10

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Observation 36ffea07-be43-4871-96e4-e8a918ea1218 · outbound

This paper cites KTO: Model Alignment as Prospect Theoretic Optimization.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation KTO: Model Alignment as Prospect Theoretic Optimization

Reference 11

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Observation fbf4ed89-55b6-4bb7-ae79-6042745a5556 · outbound

This paper cites DPOK: reinforcement learning for fine-tuning text-to-image diffusion models.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation DPOK: reinforcement learning for fine-tuning text-to-image diffusion models

Reference 12

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Observation 4dd6b9a1-9c3f-448c-b2d1-a494f4c22d5f · outbound

This paper cites Scaling laws for reward model overoptimization.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Scaling laws for reward model overoptimization

Reference 13

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Observation f405a09f-a8b8-4f0c-b4f1-24aedc49ec4e · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 14

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Observation f5386393-9f77-45d0-a062-e91a442a7eaa · outbound

This paper cites Brandt, and Tomer Michaeli.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Brandt, and Tomer Michaeli

Reference 15

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Observation 0fd283bb-8947-4ec0-a147-71c10288706d · outbound

This paper cites Denoising diffusion probabilistic models.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Denoising diffusion probabilistic models

Reference 16

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Observation 7ed99fb3-f4b9-4cb6-b824-6b90756f9817 · outbound

This paper cites Estimation of non-normalized statis- tical models by score matching.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Estimation of non-normalized statis- tical models by score matching

Reference 17

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Observation 056ea50f-2def-42a5-b416-ba63338c3fbf · outbound

This paper cites Reward learning from human preferences and demonstrations in atari.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Reward learning from human preferences and demonstrations in atari

Reference 18

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Observation 7cde6093-e2e3-4bba-999e-833d799616a2 · outbound

This paper cites Ryzhakov, Andrei Chertkov, and Ivan V.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Ryzhakov, Andrei Chertkov, and Ivan V

Reference 19

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Observation aecd68b4-bbf9-4011-8345-bf2be980306c · outbound

This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 20

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Observation 3832647f-04d5-48aa-aa2f-e45e5d773dd8 · outbound

This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 21

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Observation b884d6d8-9432-4400-bca9-162639bd102b · outbound

This paper cites Bradley Knox and Peter Stone.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Bradley Knox and Peter Stone

Reference 22

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Observation 590c7101-782e-441d-a9f4-6deb5e9fec30 · outbound

This paper cites Dif- fusion models already have A semantic latent space.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Dif- fusion models already have A semantic latent space

Reference 23

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Observation b436b6c6-7288-4fef-ba06-b6de3f4005e6 · outbound

This paper cites Aligning Diffusion Models by Optimizing Human Utility.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Aligning Diffusion Models by Optimizing Human Utility

Reference 24

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Observation c77e5e8a-10a2-4e8d-ad7e-01794de62aae · outbound

This paper cites Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

Reference 25

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Observation 7c60fc4c-8778-483e-bb79-a872fd7fb5c3 · outbound

This paper cites Let’s verify step by step.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Let’s verify step by step

Reference 26

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Observation 78f53924-dca5-4739-b167-d2751c617350 · outbound

This paper cites Lillicrap, Jonathan J.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Lillicrap, Jonathan J

Reference 27

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Observation 77126f30-7f1f-424c-8a20-51262ab6e257 · outbound

This paper cites Alignment of diffusion models: Fun- damentals, challenges, and future.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Alignment of diffusion models: Fun- damentals, challenges, and future

Reference 28

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Observation bb3b29a4-363d-41e5-a3be-f53fd5a16684 · outbound

This paper cites Interpretation and generalization of score matching.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Interpretation and generalization of score matching

Reference 29

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Observation e39b0d5b-9dfe-46f8-9c06-104af849cfbf · outbound

This paper cites Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:54:03.090375Z

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Observation e95140c4-4e3a-4518-8ae4-c28102384ea7 · outbound

This paper cites Simpo: Simple preference optimization with a reference-free reward.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Simpo: Simple preference optimization with a reference-free reward

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:54:03.078511Z

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Observation 200028e2-5bd6-4e26-bcfc-ac7eedcff526 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Playing Atari with Deep Reinforcement Learning

Reference 32

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This paper cites Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu

Reference 33

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Observation 45738fa3-cb80-498a-9e4b-6e39974ca1fc · outbound

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Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Unresolved cited work

Reference 34

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Observation 26eb8468-259e-4d7e-9703-d63950bece0a · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 35

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Observation cd834621-b559-4394-b718-9ad409ca307c · outbound

This paper cites Learning transferable visual models from natural language supervision.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Learning transferable visual models from natural language supervision

Reference 36

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Observation c30638c4-523f-41f1-b562-b0ab06fb12a4 · outbound

This paper cites Manning, Stefano Ermon, and Chelsea Finn.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Manning, Stefano Ermon, and Chelsea Finn

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:54:03.030862Z

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Observation 9087a192-90d2-47f3-8c34-8827eff92f0f · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation High-resolution image synthesis with latent diffusion models

Reference 38

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Observation 62b9beb5-4704-4174-b168-8ce82ef453e4 · outbound

This paper cites Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Reference 39

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Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Jordan, and Philipp Moritz

Reference 40

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This paper cites Proximal Policy Optimization Algorithms.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Proximal Policy Optimization Algorithms

Reference 41

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Observation 65a1d783-c4c5-4063-be56-8316d973431f · outbound

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Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Denoising diffusion models on model-based latent space

Reference 42

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Observation 65bb36dc-fe03-4a92-b4c8-cc40f26a37e0 · outbound

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Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Riedmiller

Reference 43

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Observation f3d2bf19-de5e-4fd2-bc05-c103e2f9413d · outbound

This paper cites Weiss, Niru Mah- eswaranathan, and Surya Ganguli.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Weiss, Niru Mah- eswaranathan, and Surya Ganguli

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:54:02.974017Z

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Observation dbf00f3e-f92a-4071-b3ad-45c6f05b7845 · outbound

This paper cites Prefer- ence ranking optimization for human alignment.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Prefer- ence ranking optimization for human alignment

Reference 45

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Observation 74007324-9572-4c4a-a723-9014230ea04b · outbound

This paper cites De- noising diffusion implicit models.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation De- noising diffusion implicit models

Reference 46

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This paper cites Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole

Reference 47

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Observation e71875a0-0ec2-4b2c-b1c2-4c31cca034d3 · outbound

This paper cites Ziegler, Ryan Lowe, Chelsea V oss, Alec Radford, Dario Amodei, and Paul F.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Ziegler, Ryan Lowe, Chelsea V oss, Alec Radford, Dario Amodei, and Paul F

Reference 48

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Observation 9f894b4a-0b23-46ff-80dc-3688956261a3 · outbound

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Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Sutton and Andrew G

Reference 49

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Observation b0ffe050-9b4a-4c6f-8e4d-36a5a2812329 · outbound

This paper cites A connection between score matching and denoising autoencoders.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation A connection between score matching and denoising autoencoders

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:54:02.907950Z

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Observation 00ec18d6-3d41-43a9-ba98-3860f67799ff · outbound

This paper cites Diffusion model alignment using direct preference op- timization.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Diffusion model alignment using direct preference op- timization

Reference 51

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Observation 43d4b163-c4ba-45ed-beb8-06a5d4369a6a · outbound

This paper cites Aligning Large Language Models with Human: A Survey.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Aligning Large Language Models with Human: A Survey

Reference 52

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This paper cites Dueling network architectures for deep reinforcement learning.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Dueling network architectures for deep reinforcement learning

Reference 53

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This paper cites Waytowich, Vernon Lawh- ern, and Peter Stone.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Waytowich, Vernon Lawh- ern, and Peter Stone

Reference 54

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Observation 17af7ddc-68cc-48ca-a6e1-43804bed4ab4 · outbound

This paper cites β-dpo: Direct preference optimization with dynamic β.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation β-dpo: Direct preference optimization with dynamic β

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:54:02.864885Z

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This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 56

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This paper cites Using human feedback to fine-tune diffusion models without any reward model.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Using human feedback to fine-tune diffusion models without any reward model

Reference 57

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Observation 53cef9b3-aff9-4609-86b0-0d88016a0b12 · outbound

This paper cites Diffusion models: A comprehen- sive survey of methods and applications.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Diffusion models: A comprehen- sive survey of methods and applications

Reference 58

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Observation 8b9fbbf3-1f98-450b-9fd6-2178a3f15a72 · outbound

This paper cites A dense reward view on aligning text-to-image diffusion with preference.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation A dense reward view on aligning text-to-image diffusion with preference

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:54:02.830317Z

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Observation c2b5b6be-1713-44b7-9968-af7c7e415bc3 · outbound

This paper cites RRHF: Rank Responses to Align Language Models with Human Feedback without tears.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation RRHF: Rank Responses to Align Language Models with Human Feedback without tears

Reference 60

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

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Fine-Tuning Language Models from Human Preferences

Reference 61

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Observation 22bceab4-0537-47e5-bb51-3cae2fefbeb1 · outbound

This paper cites Derivation of the Loss Function Defined in Eq.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Derivation of the Loss Function Defined in Eq

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:54:02.819504Z

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This paper cites an unresolved cited work.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Unresolved cited work

Reference 63

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Observation edc19149-b908-4958-8d5d-9ec5f162aea6 · outbound

This paper cites Implementation Details We employ a constant learning rate with a warm-up sched- ule, finalizing at 2.05 × 10−5.

Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation Implementation Details We employ a constant learning rate with a warm-up sched- ule, finalizing at 2.05 × 10−5

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:54:02.795707Z

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Pith citing papers

Observation 129b5c4a-d693-4153-bdd2-f3a363f0aa61 · inbound

Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs cites this paper.

Offline Preference Optimization for Rectified Flow with Noise-Tracked Pairs Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation

Reference 39

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Observation 794a93ca-94ab-4678-b4fc-017d4e8955e6 · inbound

Explicit Critic Guidance for Aligning Diffusion Models cites this paper.

Explicit Critic Guidance for Aligning Diffusion Models Preference Alignment for Diffusion Model via Explicit Denoised Distribution Estimation

Reference 67

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