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Paper Citation Record · LEDGER

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 4 inbound Pith citation observations for arXiv:2502.01819.

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

pith.paper-citation-record.v1
2502.01819 v3

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:29:12.477304Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T18:33:52.933672Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T18:38:53.000576Z

Reference resolution

35 of 35 outbound references displayed

  • verified exact1
  • verified fuzzy3
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9ad45e5c-fefb-4c69-b1fe-a6645a2c7d85 · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Training Diffusion Models with Reinforcement Learning

Reference 1

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Observation 8908ba33-e341-4015-9331-03746b77f918 · outbound

This paper cites Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 3

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

This paper cites Optimizing DDPM Sampling with Shortcut Fine-Tuning.

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

Reference 7

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Observation 9d31d82f-5625-46b9-8dbc-719afcb9dcc9 · outbound

This paper cites DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models

Reference 8

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source=pdf_text observed=2026-08-09T14:29:12.106970Z digest=sha256:bdbb29c0262a739cae874a48a9eb0f5e5f8edb2974cc336ddb95b1e132423ef9

Observation 7b140705-6248-492b-a55b-9936f0285c30 · outbound

This paper cites Reward-Directed Score-Based Diffusion Models via q-Learning.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Reward-Directed Score-Based Diffusion Models via q-Learning

Reference 9

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source=pdf_text observed=2026-08-09T14:29:12.111582Z digest=sha256:0d062e2351a260c938e6d29f6f038d337c20fc6a36bd7290de9baf0fc7e44913

Observation 8c78c11f-a17a-4084-bb14-1053003b7e95 · outbound

This paper cites Optimizing Prompts for Text-to-Image Generation.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Optimizing Prompts for Text-to-Image Generation

Reference 10

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source=pdf_text observed=2026-08-09T14:29:12.116193Z digest=sha256:71a57aebac6ad9d05279ab2fe4b4a2a86fda50e0890cfbecb85739864280ddde

Observation 318bb216-e0e6-40c9-ab84-9732adb0c691 · outbound

This paper cites an unresolved cited work.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Unresolved cited work

Reference 13

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Observation 188b076e-de43-4217-8cb8-be549dd71121 · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 15

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source=pdf_text observed=2026-08-09T14:29:12.137957Z digest=sha256:985128a78a290ec675b1896f519630a7b69fa7740af552c4c5296bf3898e9df0

Observation bd203227-8bd3-4fd9-a681-d2e6ad307b98 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 16

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source=pdf_text observed=2026-08-09T14:29:12.142340Z digest=sha256:a0c2ab31cf60335565f259eb5bc7d58578a9c3e0a456545a8792c0a182560f56

Observation 13bba21e-6c31-49c1-a0a5-6c9196c3db35 · outbound

This paper cites Diffusion Policy Policy Optimization.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Diffusion Policy Policy Optimization

Reference 17

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source=pdf_text observed=2026-08-09T14:29:12.146870Z digest=sha256:28176a71fb068e111e229fc6d4592cfc694317d2a08edfa5832cff0aaa0fb200

Observation b655a89a-93fb-4e3d-840d-8e4b5978885c · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Progressive Distillation for Fast Sampling of Diffusion Models

Reference 18

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source=pdf_text observed=2026-08-09T14:29:12.151666Z digest=sha256:7d53d853135b0b7b2b7c7263eb2c9a26f77d639a886fa1084d7ac102cff7f3e7

Observation f1e74cfb-e291-45f9-a336-5b54ba8f2d88 · outbound

This paper cites Improving Image Captioning with Better Use of Captions.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Improving Image Captioning with Better Use of Captions

Reference 20

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source=pdf_text observed=2026-08-09T14:29:12.282159Z digest=sha256:f00259dd3e53da31ccdbcb75ec995f869150fb6ad67eb24ed03ff554f7c056fc

Observation cefcffbe-160d-4714-8c1b-bc54d8e64a5e · outbound

This paper cites Denoising Diffusion Implicit Models.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Denoising Diffusion Implicit Models

Reference 21

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source=pdf_text observed=2026-08-09T14:29:12.359251Z digest=sha256:fe838f06b4d1a67bebb74433c2ed51546bdb6a43e2ef6e6dded3faade1dc0f71

Observation 2eed287d-df1d-4f4f-b63e-981d1047143d · outbound

This paper cites Solving Inverse Problems in Medical Imaging with Score-Based Generative Models.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

Reference 22

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source=pdf_text observed=2026-08-09T14:29:12.412419Z digest=sha256:1ea75f9208c0c7a5063695301ac3d5d8c0ed533a0724eacff0b25ad9f5d989ef

Observation 547aceb9-289b-4e0c-8fd9-df5eac3c48b9 · outbound

This paper cites Score-based Diffusion Models via Stochastic Differential Equations -- a Technical Tutorial.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Score-based Diffusion Models via Stochastic Differential Equations -- a Technical Tutorial

Reference 24

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source=pdf_text observed=2026-08-09T14:29:12.423240Z digest=sha256:3d8b6b6f038d0436d3818936f6d1239f9368de5339bec880ad5aac37f1662735

Observation 68fbedcb-4df2-4a7d-8e21-43ba93e5d1a7 · outbound

This paper cites Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control

Reference 25

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source=pdf_text observed=2026-08-09T14:29:12.427168Z digest=sha256:fa25b3443380caa01a3ff23dc827abab9ccbb7f5451265acedd882a1542574d9

Observation 71d5a541-b706-4bb9-9431-0d7e780f0a85 · outbound

This paper cites Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey

Reference 26

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local_arxiv, observed 2026-08-09T14:29:12.924859Z

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source=pdf_text observed=2026-08-09T14:29:12.431395Z digest=sha256:b02face6ccd79643ad287b349c9d8ea5a946b9d3a87425cc44c36e38dad110ec

Observation 155c2cbe-35a1-495b-9c67-dcdddbc2b032 · outbound

This paper cites GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

Reference 27

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source=pdf_text observed=2026-08-09T14:29:12.435649Z digest=sha256:318ad85ac2311b04ad9c338e0ea2530cea7ceb654c5c9ed03a9ab084cbdbb518

Observation 5a7f53eb-6642-4d76-9d21-89c598da81b0 · outbound

This paper cites Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models

Reference 28

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local_arxiv, observed 2026-08-09T14:29:12.769275Z

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source=pdf_text observed=2026-08-09T14:29:12.439940Z digest=sha256:c38bff6adb63db1f4c94be0e80d7db602098734bce4fac8fff88cbc2ed53b92f

Observation 00abb90a-3f36-4d01-a015-2da311d0e575 · outbound

This paper cites Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation

Reference 29

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Observation 6625498d-78cf-4fa1-9122-0b04808df995 · outbound

This paper cites Fast Sampling of Diffusion Models with Exponential Integrator.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Fast Sampling of Diffusion Models with Exponential Integrator

Reference 30

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Observation a6e1ead3-d69c-4063-80ae-077782e590de · outbound

This paper cites gDDIM: Generalized denoising diffusion implicit models.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning gDDIM: Generalized denoising diffusion implicit models

Reference 31

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Observation c27a3190-1fc2-4762-aa19-1da2beda719d · outbound

This paper cites Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning

Reference 32

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Observation fd453468-5b45-4d12-9523-fdef139f399e · outbound

This paper cites predicted x0.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning predicted x0

Reference 33

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 561c8ab2-6f62-410f-9e1a-749fb9e94e0b · outbound

This paper cites predicted x0.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning predicted x0

Reference 34

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Observation c14b68ca-c56b-414b-8899-dbbe163014b8 · outbound

This paper cites We adopt two ways of derivations: (a) Notice that, if we treat the ˆxθ (t, Xt) as a constant in (38) (or assume that it does not change w.r.p.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning We adopt two ways of derivations: (a) Notice that, if we treat the ˆxθ (t, Xt) as a constant in (38) (or assume that it does not change w.r.p

Reference 35

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T14:29:12.477304Z digest=sha256:bfa6769116094ac4e1339f392dbc6a90edae8931c9cd562ce6420ccb51372904

Observation 23460e6b-4a91-4898-9901-f3cdde308fc1 · outbound

This paper cites Fine-tuning of diffusion models via stochastic control: entropy regularization and beyond.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Fine-tuning of diffusion models via stochastic control: entropy regularization and beyond

Reference 1999

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source=pdf_text observed=2026-08-09T14:29:12.419252Z digest=sha256:b0c568e4c0df9a6a1233eb92ad08370f258616d85f1ec670c378f52134924e24

Observation 99790938-35c3-4b29-af97-657a3b53ebff · outbound

This paper cites Aligning Text-to-Image Models using Human Feedback.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Aligning Text-to-Image Models using Human Feedback

Reference 2009

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Observation f003bc6f-d8b6-4a98-aeef-f1bde2c43243 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 2015

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Observation 198a0a69-340c-42b8-8ad4-d38c508af818 · outbound

This paper cites Directly Fine-Tuning Diffusion Models on Differentiable Rewards.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Reference 2017

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source=pdf_text observed=2026-08-09T14:29:12.019456Z digest=sha256:b4b0846890ddfd7c2c1570f166e66b966d497a181589e58e7dd9badd9d277cc6

Observation 5097f194-b6d8-435f-a81e-d766c142c8af · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Imagen Video: High Definition Video Generation with Diffusion Models

Reference 2020

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source=pdf_text observed=2026-08-09T14:29:12.120433Z digest=sha256:b1cb7cd697f7ac51b364dcf411af82126f671ad7b926461f40f8b73c775d1f73

Observation d922e02d-ad8e-4824-bfa9-30962aeefab4 · outbound

This paper cites Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control

Reference 2021

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Observation 9aa508f7-2248-432b-b9c5-1b476b2cb3ea · outbound

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

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning LoRA: Low-Rank Adaptation of Large Language Models

Reference 2022

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Observation 27a024c8-43f9-48a7-85ab-26a0c86d5cb3 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 2023

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source=pdf_text observed=2026-08-09T14:29:11.879830Z digest=sha256:89de2f6c5bd6ec99efe175fea9c83645e3c26bf3ac302873116e289d1293922b

Observation 8082898e-1517-48f2-a77b-5a873d46bf2d · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 2024

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Pith citing papers

Observation 4e45631e-d946-4b7a-b80d-c6d1cbbcb9e7 · inbound

Flow-GRPO: Training Flow Matching Models via Online RL cites this paper.

Flow-GRPO: Training Flow Matching Models via Online RL Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning

Reference 52

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arxiv_id, observed 2026-05-11T18:45:16.728520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-11T18:45:16.641012Z digest=sha256:83868a7b746da97c339a9240fae919d142cb5eba51bd2f8eaad945e77b780247

Observation 6c6aee69-8f73-4a82-999d-d86651382aeb · inbound

Improving Text-to-Image Generation with Intrinsic Self-Confidence Rewards cites this paper.

Improving Text-to-Image Generation with Intrinsic Self-Confidence Rewards Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning

Reference 89

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arxiv_id, observed 2026-05-15T18:41:28.922983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T18:40:18.940889Z digest=sha256:ff518c80706f1c6e6daa54b67a8460616f6f36e196726aa5b7acbaa040d3309d

Observation 24c327ab-edde-46d3-8a47-fe166e38ee92 · inbound

Energy Generative Modeling: A Lyapunov-based Energy Matching Perspective cites this paper.

Energy Generative Modeling: A Lyapunov-based Energy Matching Perspective Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning

Reference 41

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metadata mismatch
arxiv_id, observed 2026-05-11T16:36:08.216028Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-09T16:05:00.985070Z digest=sha256:8cfd71f3044fef8d0eab1519cf48c0cd18153d529191aa98a50a440b0538a7ca

Observation 0e4474a6-2ce3-46b3-9e89-59faa507c1ae · inbound

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

Embedding-perturbed Exploration Preference Optimization for Flow Models Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning

Reference 97

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verified exact
arxiv_id, observed 2026-05-20T18:38:53.002168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T18:33:52.933672Z digest=sha256:185c8b17d8f4363a881f79d5b2d32f37cdd59678a9cc62ad08201aae753f8c4a