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Paper Citation Record · LEDGER

Optimizing DDPM Sampling with Shortcut Fine-Tuning

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 25 inbound Pith citation observations for arXiv:2301.13362.

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

pith.paper-citation-record.v1
2301.13362 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:44:36.629752Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:59:43.060142Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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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 54e96430-5753-4d17-8d7f-b67761e4273b · inbound

Training Diffusion Models with Reinforcement Learning cites this paper.

Training Diffusion Models with Reinforcement Learning Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 5

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verified exact
arxiv_id, observed 2026-05-11T20:16:31.002213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-11T20:16:30.840184Z digest=sha256:59ff1b5ed8398899e60cd403fa20e62f83e203cacb9ad69dc41837816708bf86

Observation f64c0a8b-9824-4093-ade5-fc045b372b82 · inbound

Diffusion Policy Policy Optimization cites this paper.

Diffusion Policy Policy Optimization Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 24

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verified exact
arxiv_id, observed 2026-05-16T08:48:14.864965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-16T08:48:14.776754Z digest=sha256:f79dce1c61d9ba349aa120fb907599fd156940cdb10fe6102119a8d1f53396c9

Observation fc6cd460-0221-4b79-904b-ce95655e2ce3 · inbound

Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable Latent-Space Surrogate Reward cites this paper.

Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable Latent-Space Surrogate Reward Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-12T14:58:37.430341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:58:37.430341Z digest=sha256:16897201f7e9b838f3b84a6817b997037f587d2fa9f814108b71403f190bed26

Observation 969d84f0-c8a0-4874-9cff-0686123ffaae · inbound

An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Models cites this paper.

An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Models Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-12T13:22:08.523393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:22:08.523393Z digest=sha256:677862b3687b161a0d96f792e9379ec72e07a9c1a5eb0616a3d9541905a4904d

Observation a8c3470b-98b8-4503-86f4-2e1df3bbd518 · inbound

Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation cites this paper.

Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-10T21:04:56.335161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:04:56.335161Z digest=sha256:9f8b99daf8a82249eda8efe85038df55f157c88301047746d4d8516f2c1db25e

Observation 46370dad-be65-46b9-ad9c-81231ea750a9 · inbound

FDPP: Fine-tune Diffusion Policy with Human Preference cites this paper.

FDPP: Fine-tune Diffusion Policy with Human Preference Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-10T20:34:07.002395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:34:07.002395Z digest=sha256:556d6583354e464f618178134a565b030b8043156bb6dbb2f9f117c6eb70173a

Observation 6e0fa41b-a1e7-49e7-9910-ac1c14611548 · inbound

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning cites this paper.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-09T14:29:12.102481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T14:29:12.102481Z digest=sha256:e2bcb01cf8f5ba397818214c5e1c88c43c98029a31500261c694e6bc0e6d2697

Observation cc40519e-4f1a-4efb-94b9-585c908972ff · inbound

Text2Stereo: Repurposing Stable Diffusion for Stereo Generation with Consistency Rewards cites this paper.

Text2Stereo: Repurposing Stable Diffusion for Stereo Generation with Consistency Rewards Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T13:26:11.346120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:26:11.346120Z digest=sha256:7d5446859ef042f69fdf9bcc46a3f6d4c769f504911d3274e9cbcf80b39e15af

Observation 9fcc7767-544d-4831-bc52-de7205cd323a · inbound

MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE cites this paper.

MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-13T13:27:50.075708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-13T13:27:50.031781Z digest=sha256:faeec78924bc0e54c40c70ed6a2929480a0c2ab64ecae2acc25abec77b1e669d

Observation 0a0e6021-ae7c-488e-988e-ff109e9d57f1 · inbound

Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning cites this paper.

Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 2023

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no resolver link, observed 2026-08-05T17:21:18.015193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T17:21:18.015193Z digest=sha256:f36d7fb32381511eadf3253c0ef4e4de825d5d07b223ce92929399cfb7699994

Observation 1f32e9d0-15c2-45a8-ba95-835510272c6c · inbound

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models cites this paper.

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-15T16:44:36.629752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:44:36.629752Z digest=sha256:bb7ccb1016b93b31b92123e39ea43f5c2d1a22309f1e496a0d5ce0ca63025163

Observation e48e195f-45f6-486e-89d3-81e54981447b · inbound

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference cites this paper.

Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-04T22:58:08.774404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:58:08.774404Z digest=sha256:f77c4913bcb0de55003b4880a9f79f8ede5ae26159a34b1f288a865748e70997

Observation ad59cff1-374c-43b4-9c84-de1300df5098 · inbound

Seeing What Matters: Visual Preference Policy Optimization for Visual Generation cites this paper.

Seeing What Matters: Visual Preference Policy Optimization for Visual Generation Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-21T18:44:18.980505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-21T18:42:29.327151Z digest=sha256:a6109313ecd08cde064b9b89676fe78ecb5df4f15b1957782992052f15b8ce45

Observation 472c39ae-af90-455c-a52e-acbb8fdb9cdd · inbound

PSR: Scaling Multi-Subject Personalized Image Generation with Pairwise Subject-Consistency Rewards cites this paper.

PSR: Scaling Multi-Subject Personalized Image Generation with Pairwise Subject-Consistency Rewards Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-17T03:51:29.354176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-17T03:49:05.489626Z digest=sha256:3a3868656a4eef72bcd78efc7054bcef940397fd0cfbd8957ffd755faa5ecbee

Observation 43ae5cd6-3dfb-46d5-833f-3cbb181c1742 · inbound

Conditional Diffusion Guidance under Hard Constraint: A Stochastic Analysis Approach cites this paper.

Conditional Diffusion Guidance under Hard Constraint: A Stochastic Analysis Approach Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-03T04:22:26.376149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T04:22:26.376149Z digest=sha256:4280ed5040fefaf14b48194429ec1353302763162b77845aac6f6783003e1841

Observation 5f0fd16e-56e0-4831-9c48-b58986a44b68 · inbound

Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generation cites this paper.

Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generation Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:16:03.306803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T02:10:26.580964Z digest=sha256:aa6f4d0f025fdddced47ef5d7038d84275a0b4214cfa8f3a7d5931ef72262d91

Observation 7c77dacb-1f03-4614-b528-441238e14771 · inbound

V-GRPO: Online Reinforcement Learning for Denoising Generative Models Is Easier than You Think cites this paper.

V-GRPO: Online Reinforcement Learning for Denoising Generative Models Is Easier than You Think Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 4

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verified exact
arxiv_id, observed 2026-05-11T20:36:11.408796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-08T08:27:17.629238Z digest=sha256:7fe74f445987c694da13c09965b57b3102267ca29a5574293a6572eaae49f060

Observation 5f8d56c2-75b2-4c63-99d6-4bf203b324f8 · inbound

Improved techniques for fine-tuning flow models via adjoint matching: a deterministic control pipeline cites this paper.

Improved techniques for fine-tuning flow models via adjoint matching: a deterministic control pipeline Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:16:10.767687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-08T09:45:00.759474Z digest=sha256:de757aeb80691a684d9b06ea2522331858aa8d679fdcfffe6b7cd137d058bae5

Observation 2a82529a-8e5a-4583-9b5c-d53b371c843f · inbound

Embedding-perturbed Exploration Preference Optimization for Flow Models cites this paper.

Embedding-perturbed Exploration Preference Optimization for Flow Models Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-20T18:38:52.956549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-20T18:33:52.933672Z digest=sha256:36830a2a056de58253e840f9f068f755fe4140769615a5fd725a446e10289f74

Observation b73b15ec-928b-4104-b8ba-5e76bc6a4620 · inbound

NaP-Control: Navigating Diffusion Prior for Versatile and Fast Character Control cites this paper.

NaP-Control: Navigating Diffusion Prior for Versatile and Fast Character Control Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-21T09:44:05.623849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-21T09:43:07.291649Z digest=sha256:978d6aacd15cac55471d9a4c02756ac64c04bfee5618f891f6d9d17783da6ada

Observation fae46b3e-80c0-46d6-b3ad-ccff0b70fbd9 · inbound

NaP-Control: Navigating Diffusion Prior for Versatile and Fast Character Control cites this paper.

NaP-Control: Navigating Diffusion Prior for Versatile and Fast Character Control Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-02T16:17:15.281961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T16:17:15.281961Z digest=sha256:eea4432ba7712461a40bf111365fd3ca163b258647d405c54ee484ea0a0826db

Observation 14c26cfb-e6cd-4480-afe5-dc0b361c884d · inbound

Curvature-Adaptive Consistency Flow Matching: Autonomous Trajectory Optimization via Reinforcement Learning cites this paper.

Curvature-Adaptive Consistency Flow Matching: Autonomous Trajectory Optimization via Reinforcement Learning Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 9

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verified exact
arxiv_id, observed 2026-07-04T08:59:43.061892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-26T10:40:22.767129Z digest=sha256:98e3e5678c234207a19c94f159417809c33b157d4ea2f1159d1a47d9049d7a3e

Observation 72e700a2-e01e-4f6a-9b16-7b96f504f8c6 · inbound

Curvature-Adaptive Consistency Flow Matching: Autonomous Trajectory Optimization via Reinforcement Learning cites this paper.

Curvature-Adaptive Consistency Flow Matching: Autonomous Trajectory Optimization via Reinforcement Learning Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-02T10:36:51.005229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T10:36:51.005229Z digest=sha256:131dab7df89eb6608c8432d8a71ff4500ffc63ac10497706a4b9bc2210fc4925

Observation afd0a2aa-1b39-46ee-a9bc-b5a1f8d2fe93 · inbound

PAPA: Online Personalized Active Preference Alignment cites this paper.

PAPA: Online Personalized Active Preference Alignment Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-07-02T16:17:08.231301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-07-02T16:16:48.255987Z digest=sha256:cc60c4b0c54a7a273aa4210490554d496f0c618893c66881ce15aa33d25dec4c

Observation b71b9d44-1528-404e-8a2e-6598c7adcf4a · inbound

Optimizing Visual Generative Models via Distribution-wise Rewards cites this paper.

Optimizing Visual Generative Models via Distribution-wise Rewards Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-07-03T16:48:39.776342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-07-03T16:39:12.711424Z digest=sha256:a1877968137f654526a41893e1785f24240629dd3ee3bec1da20b4091e142c70