Paper Citation Record · LEDGER

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation

As of 8 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2505.14821.

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

pith.paper-citation-record.v1
2505.14821 v1

Coverage vector

measured 62 of 62 reference resolution

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One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T11:59:18.223000Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:19:43.736761Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation 31abe703-6ff6-4548-856e-846249918849 · outbound

This paper cites Improved algorithms for linear stochastic bandits.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Improved algorithms for linear stochastic bandits

Reference 1

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Observation 2cc0f559-aa62-41e0-9463-6412f645b2e1 · outbound

This paper cites Efficient optimistic exploration in linear-quadratic regulators via lagrangian relaxation.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Efficient optimistic exploration in linear-quadratic regulators via lagrangian relaxation

Reference 2

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Observation e7fb566b-ff05-47b9-815f-a17bcdbcc7ca · outbound

This paper cites Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 3

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This paper cites Finite-time analysis of the multiarmed bandit problem, 2002.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Finite-time analysis of the multiarmed bandit problem, 2002

Reference 4

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This paper cites Provably efficient q-learning with low switching cost.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Provably efficient q-learning with low switching cost

Reference 5

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Observation c955c716-c5a9-4388-bb25-f1680efa5177 · outbound

This paper cites Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon

Reference 6

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Observation 21e0f825-ca2a-4fa2-bc77-b1473cde29ae · outbound

This paper cites The stability of solutions of linear differential equations.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation The stability of solutions of linear differential equations

Reference 7

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Observation 6b2f0766-dd42-4309-91ab-d338196e7f14 · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Training Diffusion Models with Reinforcement Learning

Reference 8

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This paper cites Variational inference: A review for statisticians.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Variational inference: A review for statisticians

Reference 9

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Observation b21b9bac-380b-4d06-b489-84378a9f8539 · outbound

This paper cites OpenAI Gym.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation OpenAI Gym

Reference 10

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Observation 3e957e68-8569-4ba6-9f3b-52618ed56dff · outbound

This paper cites Caines and David Levanony.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Caines and David Levanony

Reference 11

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Observation d93d14fc-37a0-44d2-abb3-8924c4de9d5b · outbound

This paper cites Online learning with switching costs and other adaptive adversaries.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Online learning with switching costs and other adaptive adversaries

Reference 12

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Observation c6c64d36-d5cf-438c-b79b-758a7ef020ac · outbound

This paper cites Neural ordinary differential equations.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Neural ordinary differential equations

Reference 13

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This paper cites Online linear quadratic control.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Online linear quadratic control

Reference 14

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Observation 041f687a-14ac-4dee-a887-64a53dbcbe3a · outbound

This paper cites Reinforcement learning in continuous time and space.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Reinforcement learning in continuous time and space

Reference 15

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Observation b1d2a57e-f7af-4e2e-81f8-98e72c58d1c5 · outbound

This paper cites A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost

Reference 16

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Observation 1fc92e53-bbbf-4cd9-9e70-e367d4ed1754 · outbound

This paper cites Hamiltonian neural networks.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Hamiltonian neural networks

Reference 17

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Observation 21af838b-f18f-47ef-a737-d445ab0bc8cf · outbound

This paper cites Nearly minimax optimal reinforcement learning for linear markov decision processes.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Nearly minimax optimal reinforcement learning for linear markov decision processes

Reference 18

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Observation e4036064-d016-4112-b852-94334e775d14 · outbound

This paper cites Denoising diffusion probabilistic models.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Denoising diffusion probabilistic models

Reference 19

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This paper cites Active observing in continuous-time control.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Active observing in continuous-time control

Reference 20

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Observation 255b7f55-7f02-4aca-8a34-25c15c2acc10 · outbound

This paper cites Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality

Reference 21

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This paper cites Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems

Reference 22

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Observation e0c9ea22-7e44-4783-972a-3b766667bb7f · outbound

This paper cites Bellman eluder dimension: New rich classes of rl problems, and sample-efficient algorithms.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Bellman eluder dimension: New rich classes of rl problems, and sample-efficient algorithms

Reference 23

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Observation 93762ce8-1451-4622-87b8-47c2f943a7fc · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Adam: A Method for Stochastic Optimization

Reference 24

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This paper cites Online Sub-Sampling for Reinforcement Learning with General Function Approximation.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Online Sub-Sampling for Reinforcement Learning with General Function Approximation

Reference 25

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This paper cites Flow Matching for Generative Modeling.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Flow Matching for Generative Modeling

Reference 26

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This paper cites I ^2 sb: Image-to-image schr \"o dinger bridge.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation I ^2 sb: Image-to-image schr \"o dinger bridge

Reference 27

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This paper cites Let us Build Bridges: Understanding and Extending Diffusion Generative Models.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Let us Build Bridges: Understanding and Extending Diffusion Generative Models

Reference 28

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation A convnet for the 2020s

Reference 29

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Value iteration in continuous actions, states and time

Reference 30

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This paper cites Numerical solution of stochastic differential equations with jumps in finance, volume 64.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Numerical solution of stochastic differential equations with jumps in finance, volume 64

Reference 31

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This paper cites Aligning Text-to-Image Diffusion Models with Reward Backpropagation.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Aligning Text-to-Image Diffusion Models with Reward Backpropagation

Reference 32

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever

Reference 33

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation High-resolution image synthesis with latent diffusion models

Reference 34

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Linear bandits with limited adaptivity and learning distributional optimal design

Reference 35

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Eluder dimension and the sample complexity of optimistic exploration

Reference 36

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Schuhmann

Reference 37

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation LAION -5b: An open large-scale dataset for training next generation image-text models

Reference 38

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Diffusion schr \"o dinger bridge matching

Reference 39

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Online reinforcement learning in stochastic continuous-time systems

Reference 40

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Naive exploration is optimal for online lqr

Reference 41

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Deep unsupervised learning using nonequilibrium thermodynamics

Reference 42

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Aligned diffusion schr \"o dinger bridges

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:37:10.593886Z

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Observation 52534e3d-f3fb-436a-8c23-5ce0273270f1 · outbound

This paper cites Denoising Diffusion Implicit Models.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Denoising Diffusion Implicit Models

Reference 44

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This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Score-Based Generative Modeling through Stochastic Differential Equations

Reference 45

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

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Optimal scheduling of entropy regularizer for continuous-time linear-quadratic reinforcement learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:37:10.364103Z

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Observation e99bb684-77a3-4031-88da-12c510c0a0a2 · outbound

This paper cites Improving and generalizing flow-based generative models with minibatch optimal transport.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Improving and generalizing flow-based generative models with minibatch optimal transport

Reference 47

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This paper cites Efficient exploration in continuous-time model-based reinforcement learning.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Efficient exploration in continuous-time model-based reinforcement learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:37:10.158591Z

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Observation f83f1bff-2f1c-4b3c-b84d-43aa19dc913a · outbound

This paper cites When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:37:07.667326Z

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Observation b5f7337d-44a6-4d94-bcb9-fcb9da649da3 · outbound

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

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Feedback Efficient Online Fine-Tuning of Diffusion Models

Reference 50

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This paper cites Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:37:09.997520Z

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Observation 7702d10a-3c37-4c0a-a95a-49aade64764d · outbound

This paper cites The benefits of being distributional: Small-loss bounds for reinforcement learning.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation The benefits of being distributional: Small-loss bounds for reinforcement learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:37:09.750686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b3a74679-9960-4c7e-95c4-6f52117c8263 · outbound

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

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Provably efficient reinforcement learning with linear function approximation under adaptivity constraints

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:37:09.484417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation acda09f0-934e-445c-990c-f1a439f68b17 · outbound

This paper cites Pytorch image models.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Pytorch image models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T15:37:06.270726Z

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Observation 164b93c3-0d70-46fb-b4c8-203ef5fbaca4 · outbound

This paper cites Difffit: Unlocking transferability of large diffusion models via simple parameter-efficient fine-tuning.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Difffit: Unlocking transferability of large diffusion models via simple parameter-efficient fine-tuning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:37:09.232604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f32868e0-e755-4191-bbc9-c95217842739 · outbound

This paper cites A general framework for sequential decision-making under adaptivity constraints.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation A general framework for sequential decision-making under adaptivity constraints

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:37:08.969253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d1a177c7-4a94-48c0-a660-f323fba03955 · outbound

This paper cites a hdesm \.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation a hdesm \

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:37:08.717697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 54937fe4-15bf-42f9-88b6-5fb6f8e9f34f · outbound

This paper cites Censored sampling of diffusion models using 3 minutes of human feedback.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Censored sampling of diffusion models using 3 minutes of human feedback

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:37:08.519371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c5d5841b-dfd4-4569-b0c2-7d817593d2e1 · outbound

This paper cites Is reinforcement learning more difficult than bandits? a near-optimal algorithm escaping the curse of horizon.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Is reinforcement learning more difficult than bandits? a near-optimal algorithm escaping the curse of horizon

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T15:37:07.034256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 656dc45f-26bf-4352-a7ea-4a1f85646e7e · outbound

This paper cites Improved variance-aware confidence sets for linear bandits and linear mixture mdp.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Improved variance-aware confidence sets for linear bandits and linear mixture mdp

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T15:37:07.113976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1488d572-2bef-4de6-9d25-36dfcf8e73b1 · outbound

This paper cites A nearly optimal and low-switching algorithm for reinforcement learning with general function approximation.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation A nearly optimal and low-switching algorithm for reinforcement learning with general function approximation

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T15:37:07.256981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fa972cc-2468-4cac-b7ab-1e912c9f9142 · outbound

This paper cites Unsupervised learning of lagrangian dynamics from images for prediction and control.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation Unsupervised learning of lagrangian dynamics from images for prediction and control

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:37:08.235545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation cbb84136-02e0-4a41-85f4-0d7c5d34707a · inbound

PhiBE-Q-Learning: Bridging Off-Policy Reinforcement Learning and Continuous-Time Control cites this paper.

PhiBE-Q-Learning: Bridging Off-Policy Reinforcement Learning and Continuous-Time Control Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:19:43.738291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.