Paper Citation Record · LEDGER

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence

As of 16 August 2026, this Paper Citation Record lists 81 of 81 outbound references and 2 inbound Pith citation observations for arXiv:2412.18164.

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

pith.paper-citation-record.v1
2412.18164 v4

Coverage vector

measured 81 of 81 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:06:05.638607Z

measured 83 of 83 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:44:36.655600Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T22:54:25.513562Z

Reference resolution

81 of 81 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fa8d8526-c9d5-4a48-9711-e329351a6f43 · outbound

This paper cites A green colored rabbit.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence A green colored rabbit

Reference 1

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This paper cites A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

Reference 2

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Observation 7cb003e6-f3e9-4279-916e-22b647fd6006 · outbound

This paper cites A systematic review on data scarcity problem in deep learning: solution and applications.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence A systematic review on data scarcity problem in deep learning: solution and applications

Reference 3

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Observation e74cd2a1-5d11-4afe-960e-c0b1cdb8248f · outbound

This paper cites An optimal control perspective on diffusion-based generative modeling.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence An optimal control perspective on diffusion-based generative modeling

Reference 4

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This paper cites Improving image generation with better captions.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Improving image generation with better captions

Reference 5

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Observation ac9987fa-ff55-4780-89a5-92475bf9f1e6 · outbound

This paper cites Four wolves in the park.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Four wolves in the park

Reference 6

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Observation 9e31e720-ff2c-4690-b87a-92848b4a9223 · outbound

This paper cites Global optimality guarantees for policy gradient methods.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Global optimality guarantees for policy gradient methods

Reference 7

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Observation 0350f03f-1020-4464-a58d-f3ff84418e95 · outbound

This paper cites Training diffusion models with reinforcement learning.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Training diffusion models with reinforcement learning

Reference 8

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Observation 15e89844-91f6-4bf6-9409-39799aec455b · outbound

This paper cites LQR through the Lens of First Order Methods: Discrete-time Case.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence LQR through the Lens of First Order Methods: Discrete-time Case

Reference 9

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Observation 0d4699b2-d9ea-4e58-8f6a-ebc841be060e · outbound

This paper cites Fast global convergence of natural policy gradient methods with entropy regularization.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Fast global convergence of natural policy gradient methods with entropy regularization

Reference 10

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Observation 8e618e5d-9bc6-47c5-ae61-5e3af5a42fad · outbound

This paper cites Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Reference 11

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Observation cf9e6dc4-c718-4532-a845-95c29af2eb94 · outbound

This paper cites Diffusion models beat GANs on image synthesis.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Diffusion models beat GANs on image synthesis

Reference 12

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Observation b9f9fa83-6185-486a-9735-a35f71bb01cd · outbound

This paper cites Jovanovi´ c.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Jovanovi´ c

Reference 13

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Observation e664c4c4-ffd5-46b6-8b40-ab5ffdf39b72 · outbound

This paper cites Tutorial on Variational Autoencoders.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Tutorial on Variational Autoencoders

Reference 14

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Observation 3adbb9e3-d812-44ac-8332-bf2b2c959e36 · outbound

This paper cites Adjoint matching: Fine- tuning flow and diffusion generative models with memoryless stochastic optimal control.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Adjoint matching: Fine- tuning flow and diffusion generative models with memoryless stochastic optimal control

Reference 15

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Observation 3117154c-ee1e-48a9-999c-56c3a2593c54 · outbound

This paper cites Generative Adversarial Network (GAN): A general review on different variants of GAN and applications.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Generative Adversarial Network (GAN): A general review on different variants of GAN and applications

Reference 16

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Observation cc1f9125-9576-43be-a8a7-c16eed7f8d67 · outbound

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

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Optimizing DDPM Sampling with Shortcut Fine-Tuning

Reference 17

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Observation a21d6073-82a7-4df9-afab-51168ffc2dd6 · outbound

This paper cites Reinforcement learning for fine-tuning text-to-image diffusion models.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Reinforcement learning for fine-tuning text-to-image diffusion models

Reference 18

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Observation e5d4075c-48bb-42f4-b3e7-7493e8d7895c · outbound

This paper cites Stochastic policy gradient methods: Improved sample complexity for fisher-non-degenerate policies.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Stochastic policy gradient methods: Improved sample complexity for fisher-non-degenerate policies

Reference 19

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Observation 958e1eb6-6d35-42bf-9fa4-35036f72a340 · outbound

This paper cites Global convergence of policy gradient methods for the linear quadratic regulator.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Global convergence of policy gradient methods for the linear quadratic regulator

Reference 20

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Observation cdb50f85-04e8-4424-9f49-bab21e9b44d9 · outbound

This paper cites Understanding the limita- tions of conditional generative models.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Understanding the limita- tions of conditional generative models

Reference 21

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Observation d32cc061-ec27-4472-9800-ad0e0d8b693a · outbound

This paper cites Real analysis: modern techniques and their applications , volume 40.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Real analysis: modern techniques and their applications , volume 40

Reference 22

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Observation 6f8fda2d-7f17-4fbc-8ff8-bd9f32408464 · outbound

This paper cites Single-timescale actor-critic provably finds glob- ally optimal policy.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Single-timescale actor-critic provably finds glob- ally optimal policy

Reference 23

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Observation d36365a9-3dc7-4d14-817f-09fc4a655c6f · outbound

This paper cites Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehen- sive review.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehen- sive review

Reference 24

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Observation 8d718361-ca1f-40b9-bf28-1aedfed08ead · outbound

This paper cites Scaling laws for reward model overoptimization.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Scaling laws for reward model overoptimization

Reference 25

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Observation 429bb7ef-a00d-4b33-93e2-974a1c426035 · outbound

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

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Reward-Directed Score-Based Diffusion Models via q-Learning

Reference 26

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This paper cites Fast policy learning for linear quadratic regulator with entropy regularization.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Fast policy learning for linear quadratic regulator with entropy regularization

Reference 27

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Observation cfaddf97-f300-4542-ae57-2e2546cc5a57 · outbound

This paper cites Policy gradient methods for the noisy linear quadratic regulator over a finite horizon.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Policy gradient methods for the noisy linear quadratic regulator over a finite horizon

Reference 28

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Observation 787f1481-909c-4515-94f2-add56ddd11f1 · outbound

This paper cites Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators

Reference 29

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This paper cites Neural network-based score estimation in diffusion models: Optimization and generalization.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Neural network-based score estimation in diffusion models: Optimization and generalization

Reference 30

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Observation 6b737c60-0ec3-4a3d-bff2-5bdb588350ca · outbound

This paper cites Classifier-free diffusion guidance.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Classifier-free diffusion guidance

Reference 31

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Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Unresolved cited work

Reference 32

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This paper cites LoRA: Low-rank adaptation of large language models.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence LoRA: Low-rank adaptation of large language models

Reference 33

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Observation 28a1d883-c37e-4644-8e0f-f552c78108b4 · outbound

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Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Denoising diffusion probabilistic models

Reference 34

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This paper cites Neural tangent kernel: Convergence and general- ization in neural networks.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Neural tangent kernel: Convergence and general- ization in neural networks

Reference 35

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Observation aea09c14-654b-4a9a-9702-0e0fc8dd7a93 · outbound

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

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Estimation of non-normalized statistical models by score matching

Reference 36

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

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Aligning Text-to-Image Models using Human Feedback

Reference 37

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Observation fc9744f6-4f71-47bb-9ecd-27d9e7456071 · outbound

This paper cites Provably efficient reinforcement learning with linear function approximation.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Provably efficient reinforcement learning with linear function approximation

Reference 38

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Observation 59165ce3-98dd-46df-beae-8750a6abbf16 · outbound

This paper cites Neural trust region/proximal policy optimization attains globally optimal policy.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Neural trust region/proximal policy optimization attains globally optimal policy

Reference 39

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This paper cites Towards non-asymptotic convergence for diffusion- based generative models.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Towards non-asymptotic convergence for diffusion- based generative models

Reference 40

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Observation bfcca9c3-49bd-4da1-80ca-1652a208d86d · outbound

This paper cites Convergence Analysis for Entropy-Regularized Control Problems: A Probabilistic Approach.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Convergence Analysis for Entropy-Regularized Control Problems: A Probabilistic Approach

Reference 41

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This paper cites An improved analysis of (variance-reduced) policy gradient and natural policy gradient methods.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence An improved analysis of (variance-reduced) policy gradient and natural policy gradient methods

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:07.079791Z

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Observation a7493859-4bc6-4a7e-8991-0a77bf8aa73f · outbound

This paper cites Global expo- nential convergence of gradient methods over the nonconvex landscape of the linear quadratic regulator.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Global expo- nential convergence of gradient methods over the nonconvex landscape of the linear quadratic regulator

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:07.036962Z

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This paper cites Bartlett, and Martin J.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Bartlett, and Martin J

Reference 44

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Observation 4a6f0fe6-733d-4045-81fc-db085f31bbf0 · outbound

This paper cites On Bellman equations for continuous-time policy evaluation I: discretization and approximation.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence On Bellman equations for continuous-time policy evaluation I: discretization and approximation

Reference 45

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Source-reported events for the cited work

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Observation 503e6792-8832-4c44-b414-e07b3b97ebbb · outbound

This paper cites Improved sample complexity analysis of natural policy gra- dient algorithm with general parameterization for infinite horizon discounted reward markov decision processes.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Improved sample complexity analysis of natural policy gra- dient algorithm with general parameterization for infinite horizon discounted reward markov decision processes

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:07.018645Z

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

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 47

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This paper cites Sora: Creating video from text.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Sora: Creating video from text

Reference 48

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

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 49

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Observation 02957637-6a41-49b4-a8bd-4292a31d0272 · outbound

This paper cites Random features for large-scale kernel machines.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Random features for large-scale kernel machines

Reference 50

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Observation 2ffaaf0d-b06c-4dba-9a64-b8a739bbb37a · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 51

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Observation 52a93bb1-b5a8-4bb7-95bc-062aecf02d32 · outbound

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

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence High- resolution image synthesis with latent diffusion models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.973206Z

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Observation 168015d2-f089-46c7-b55e-8d9704752e3e · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Deep unsupervised learning using nonequilibrium thermodynamics

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T05:06:05.273826Z

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Observation 539e4eaa-c2d2-45ee-98ed-960bad2cf1bc · outbound

This paper cites Low-rank adaptation for fast text-to-image diffusion fine-tuning.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Low-rank adaptation for fast text-to-image diffusion fine-tuning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.934866Z

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Observation f4d21df3-4525-4f25-b01f-28f399decc63 · outbound

This paper cites Score-based generative modeling through stochastic differential equations.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Score-based generative modeling through stochastic differential equations

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.895826Z

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Observation a58279c3-5623-449c-a46c-d19a4c82bd0f · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Generative modeling by estimating gradients of the data distribution

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T05:06:05.278921Z

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Observation 667f8fb7-24ad-431f-8a4b-2cb942244e39 · outbound

This paper cites Exploration-exploitation trade-off for continuous-time episodic reinforcement learning with linear-convex models.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Exploration-exploitation trade-off for continuous-time episodic reinforcement learning with linear-convex models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T05:06:05.304761Z

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Observation cd84866f-5688-4c4a-a113-f206db659669 · outbound

This paper cites Support vector machines.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Support vector machines

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T05:06:05.288274Z

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Observation 5e6f4510-01a0-4f55-af16-e69ca2f95973 · outbound

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

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Fine-tuning of diffusion models via stochastic control: entropy regularization and beyond

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T05:06:05.365150Z

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Observation dad2ee27-4993-4e4d-95ac-467de4b2b2eb · outbound

This paper cites Optimal scheduling of entropy regularizer for continuous-time linear-quadratic reinforcement learning.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Optimal scheduling of entropy regularizer for continuous-time linear-quadratic reinforcement learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.871493Z

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Observation 4b0dd730-8fef-4e84-8a1d-b8ef90f36887 · outbound

This paper cites Feedback efficient online fine- tuning of diffusion models.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Feedback efficient online fine- tuning of diffusion models

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.853852Z

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Observation 9e412924-b024-47c8-9f43-ff86adb32b95 · outbound

This paper cites Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T05:06:05.371203Z

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Observation fa2cf287-f0b6-4139-b824-e35eaca26f05 · outbound

This paper cites Diffusion model alignment using direct pref- erence optimization.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Diffusion model alignment using direct pref- erence optimization

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.838016Z

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Observation a5cf05a3-2809-43d7-a9b3-c4c162083f2a · outbound

This paper cites Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T05:06:05.398473Z

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Observation 5287573f-d3b6-4adc-acbf-3f7d532e7989 · outbound

This paper cites Neural policy gradient methods: Global optimality and rates of convergence.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Neural policy gradient methods: Global optimality and rates of convergence

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.781968Z

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Observation 80d3efde-abf2-4a1b-83a8-0f6ada74f749 · outbound

This paper cites Reinforcement learning in continuous time and space: A stochastic control approach.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Reinforcement learning in continuous time and space: A stochastic control approach

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.815846Z

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Observation f61ee642-85a2-41b2-bad2-ca0047b8c096 · outbound

This paper cites On the convergence rates of policy gradient methods.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence On the convergence rates of policy gradient methods

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.740111Z

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Observation cd62b26a-a901-4a97-b1be-7059df215b7c · outbound

This paper cites De novo design of protein structure and function with rfdiffusion.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence De novo design of protein structure and function with rfdiffusion

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T05:06:05.470180Z

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Observation f7d49c05-905e-4b5d-95e4-97cf6e00748e · outbound

This paper cites Doubly robust off-policy actor-critic: Convergence and optimality.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Doubly robust off-policy actor-critic: Convergence and optimality

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.696704Z

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Observation eee13c1a-6374-44aa-a353-5beb502f1939 · outbound

This paper cites Imagereward: Learning and evaluating human preferences for text-to-image generation.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Imagereward: Learning and evaluating human preferences for text-to-image generation

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.714562Z

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Observation ef0c28ac-8c30-4151-8782-b730a223912a · outbound

This paper cites Some fine properties of backward stochastic differential equations.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Some fine properties of backward stochastic differential equations

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.651255Z

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Observation d6af2827-3c8c-4cdd-8d65-fbcbd6e29c15 · outbound

This paper cites Policy mirror descent for regularized reinforcement learning: A generalized framework with linear convergence.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Policy mirror descent for regularized reinforcement learning: A generalized framework with linear convergence

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.672312Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 941658cb-3559-4e9e-b81f-f6688f64c35e · outbound

This paper cites Variational policy gradient method for reinforcement learning with general utilities.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Variational policy gradient method for reinforcement learning with general utilities

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.602432Z

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Observation 7be320c7-903d-435c-90a1-538fe2dbc0d1 · outbound

This paper cites Backward stochastic differential equations.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Backward stochastic differential equations

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.631399Z

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Observation c0f187bd-8675-4452-9bc2-fd375ebfa5a5 · outbound

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

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-11T05:06:05.597764Z

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Observation 26d2adc9-7017-480a-b1f6-663ab75d96a3 · outbound

This paper cites Provably efficient actor-critic for risk-sensitive and robust adversarial RL: A linear-quadratic case.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Provably efficient actor-critic for risk-sensitive and robust adversarial RL: A linear-quadratic case

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.574629Z

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Observation 7f7ba3cb-de81-422d-89ca-aa4de99dd92f · outbound

This paper cites A Policy Gradient Framework for Stochastic Optimal Control Problems with Global Convergence Guarantee.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence A Policy Gradient Framework for Stochastic Optimal Control Problems with Global Convergence Guarantee

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-11T05:06:05.612435Z

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Observation f3511787-1050-4708-9488-ff946b21a301 · outbound

This paper cites Adding Conditional Control to Diffusion Models with Reinforcement Learning.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Adding Conditional Control to Diffusion Models with Reinforcement Learning

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-11T05:06:05.605157Z

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Observation 18c3c241-21f8-4dea-aaa3-736faae3942a · outbound

This paper cites Solving Time-Continuous Stochastic Optimal Control Problems: Algorithm Design and Convergence Analysis of Actor-Critic Flow.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Solving Time-Continuous Stochastic Optimal Control Problems: Algorithm Design and Convergence Analysis of Actor-Critic Flow

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-11T05:06:05.638607Z

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Observation 0be7e781-41a6-4c3c-969a-e7849765117d · outbound

This paper cites Single timescale actor-critic method to solve the linear quadratic regulator with convergence guarantees.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Single timescale actor-critic method to solve the linear quadratic regulator with convergence guarantees

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:06:06.550221Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1dde8b53-9889-4672-af7e-be11d868b13a · outbound

This paper cites an unresolved cited work.

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-11T05:06:07.606570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation f14339bd-36ae-4dd6-914c-2cd9e7c7f5ac · inbound

Statistical guarantees for continuous-time policy evaluation: blessing of ellipticity and new tradeoffs cites this paper.

Statistical guarantees for continuous-time policy evaluation: blessing of ellipticity and new tradeoffs Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-08T22:54:25.519427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 86941e3d-e0ef-4513-8728-6c54adc717d2 · 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 Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T16:44:36.655600Z

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