Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models | Paper Citation Record

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This paper cites A tail inequality for suprema of unbounded empirical processes with applications to markov chains.

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models A tail inequality for suprema of unbounded empirical processes with applications to markov chains

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 2

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Surprising Negative Results for Generative Adversarial Tree Search

Reference 3

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 4

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This paper cites Accelerating RL for LLM Reasoning with Optimal Advantage Regression.

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Accelerating RL for LLM Reasoning with Optimal Advantage Regression

Reference 5

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Training Diffusion Models with Reinforcement Learning

Reference 6

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Approximation variationnelle des probl \`e mes aux limites

Reference 7

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 9

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Tail bounds via generic chaining

Reference 10

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This paper cites Dhariwal and A.

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Dhariwal and A

Reference 11

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 12

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

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

Reference 13

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 14

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This paper cites Deep neural network approximation for high-dimensional parabolic Hamilton-Jacobi-Bellman equations.

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Deep neural network approximation for high-dimensional parabolic Hamilton-Jacobi-Bellman equations

Reference 15

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Reward-Directed Score-Based Diffusion Models via q-Learning

Reference 16

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 17

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 18

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This paper cites Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence.

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

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This paper cites Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control.

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control

Reference 20

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 21

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This paper cites Jia and X.

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Jia and X

Reference 22

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Jia and X

Reference 23

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Jia and X

Reference 24

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 25

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Korshunova, N

Reference 26

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Kakade and J

Reference 27

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Koltchinskii

Reference 28

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 29

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This paper cites Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality.

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality

Reference 30

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Reference 31

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Learning subgaussian classes : Upper and minimax bounds

Reference 32

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Estimates of the numerical density for stochastic differential equations with multiplicative noise

Reference 33

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Madelung

Reference 34

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Munos and P

Reference 35

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Mendelson

Reference 36

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Muhle-Karbe, J

Reference 37

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

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Statistical guarantees for continuous-time policy evaluation: blessing of ellipticity and new tradeoffs

Reference 38

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Optimal oracle inequalities for projected fixed-point equations, with applications to policy evaluation

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Menozzi, A

Reference 40

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models On Bellman equations for continuous-time policy evaluation I: discretization and approximation

Reference 41

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Nemirovski, A

Reference 42

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models The Malliavin calculus and related topics

Reference 43

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Ouyang, J

Reference 44

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 45

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Sirignano and K

Reference 46

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 47

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Theodorou, J

Reference 50

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Tang and R

Reference 52

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

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control

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This paper cites Feedback Efficient Online Fine-Tuning of Diffusion Models.

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Feedback Efficient Online Fine-Tuning of Diffusion Models

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 56

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Weisz, P

Reference 57

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 58

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Xie and N

Reference 59

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 60

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning

Reference 61

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 62

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

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Fine-Tuning Language Models from Human Preferences

Reference 63

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Ziemann, S

Reference 64

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Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Unresolved cited work

Reference 65

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