VINE: Taming Generative Control Policies for Reinforcement Learning | Paper Citation Record

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Reference 1

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 2

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Observation d52eb109-f24f-46a2-9be7-408248a22a4e · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

VINE: Taming Generative Control Policies for Reinforcement Learning $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 3

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This paper cites $\pi^{*}_{0.6}$: a VLA That Learns From Experience.

VINE: Taming Generative Control Policies for Reinforcement Learning $\pi^{*}_{0.6}$: a VLA That Learns From Experience

Reference 4

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 5

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Observation 8e3f693e-8fbe-45cb-af02-16391c9e5f8a · outbound

This paper cites Zhang, C.

VINE: Taming Generative Control Policies for Reinforcement Learning Zhang, C

Reference 6

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 7

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 8

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Observation a78e56bd-2d28-4f0e-870d-50abbf61d4ca · outbound

This paper cites ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training.

VINE: Taming Generative Control Policies for Reinforcement Learning ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Training

Reference 9

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 10

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This paper cites an unresolved cited work.

VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 11

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Observation 2357260e-8191-4294-953a-bf3fb003962f · outbound

This paper cites Zhang, S.

VINE: Taming Generative Control Policies for Reinforcement Learning Zhang, S

Reference 12

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Observation 50e6bac8-5ece-4ef7-b4fa-e1371a16fe65 · outbound

This paper cites Li and S.

VINE: Taming Generative Control Policies for Reinforcement Learning Li and S

Reference 13

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 14

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Observation 1a4037ea-2f6e-4467-b041-ba9c88377968 · outbound

This paper cites Psenka, A.

VINE: Taming Generative Control Policies for Reinforcement Learning Psenka, A

Reference 15

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Observation 1bea8a4a-abff-4ac5-9217-b20ffffe553d · outbound

This paper cites Dhariwal and A.

VINE: Taming Generative Control Policies for Reinforcement Learning Dhariwal and A

Reference 16

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 17

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 18

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This paper cites Wagenmaker, M.

VINE: Taming Generative Control Policies for Reinforcement Learning Wagenmaker, M

Reference 19

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 20

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This paper cites EXPO: Stable Reinforcement Learning with Expressive Policies.

VINE: Taming Generative Control Policies for Reinforcement Learning EXPO: Stable Reinforcement Learning with Expressive Policies

Reference 21

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 22

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Observation 48ed7a5d-2b4a-4819-81cd-b5855a481e28 · outbound

This paper cites IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies.

VINE: Taming Generative Control Policies for Reinforcement Learning IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies

Reference 23

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 24

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Observation 54ba558f-9cb8-4c17-b7db-0154713494a2 · outbound

This paper cites RL Token: Bootstrapping Online RL with Vision-Language-Action Models.

VINE: Taming Generative Control Policies for Reinforcement Learning RL Token: Bootstrapping Online RL with Vision-Language-Action Models

Reference 25

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Observation b3fbc09c-86a0-43de-84e9-4e1179feb6b1 · outbound

This paper cites Training Diffusion Policies via Prior-Mapping Co-Evolution.

VINE: Taming Generative Control Policies for Reinforcement Learning Training Diffusion Policies via Prior-Mapping Co-Evolution

Reference 26

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Observation d63c9ed7-7f00-4cac-bf17-822edc0286fc · outbound

This paper cites FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation.

VINE: Taming Generative Control Policies for Reinforcement Learning FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation

Reference 27

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Observation 405a6e6e-a03f-4800-8f5c-ed1fd658b44e · outbound

This paper cites Behavior Regularized Offline Reinforcement Learning.

VINE: Taming Generative Control Policies for Reinforcement Learning Behavior Regularized Offline Reinforcement Learning

Reference 28

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This paper cites Fujimoto and S.

VINE: Taming Generative Control Policies for Reinforcement Learning Fujimoto and S

Reference 29

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VINE: Taming Generative Control Policies for Reinforcement Learning Tarasov, A

Reference 30

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 31

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 32

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VINE: Taming Generative Control Policies for Reinforcement Learning Lipman, R

Reference 33

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 34

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 35

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 36

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 37

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 38

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VINE: Taming Generative Control Policies for Reinforcement Learning FlowDPG: Deterministic Policy Gradient on Flow Matching Policies for Real-World Manipulation

Reference 39

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VINE: Taming Generative Control Policies for Reinforcement Learning Silver, G

Reference 40

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VINE: Taming Generative Control Policies for Reinforcement Learning Fujimoto, D

Reference 41

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VINE: Taming Generative Control Policies for Reinforcement Learning Kumar, J

Reference 42

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This paper cites Ding and C.

VINE: Taming Generative Control Policies for Reinforcement Learning Ding and C

Reference 43

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 44

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VINE: Taming Generative Control Policies for Reinforcement Learning Scaling Offline RL via Efficient and Expressive Shortcut Models

Reference 45

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 46

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 47

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VINE: Taming Generative Control Policies for Reinforcement Learning Barreiros, A

Reference 48

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VINE: Taming Generative Control Policies for Reinforcement Learning Diffusion Models for Reinforcement Learning: A Survey

Reference 49

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VINE: Taming Generative Control Policies for Reinforcement Learning Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review

Reference 50

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VINE: Taming Generative Control Policies for Reinforcement Learning Zhang, W

Reference 51

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 52

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 53

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 54

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VINE: Taming Generative Control Policies for Reinforcement Learning Diffusion Guidance Is a Controllable Policy Improvement Operator

Reference 55

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VINE: Taming Generative Control Policies for Reinforcement Learning Quantum optimal transport with convex regularization

Reference 56

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VINE: Taming Generative Control Policies for Reinforcement Learning Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control

Reference 57

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VINE: Taming Generative Control Policies for Reinforcement Learning Bergmeister, S

Reference 58

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VINE: Taming Generative Control Policies for Reinforcement Learning Improved techniques for fine-tuning flow models via adjoint matching: a deterministic control pipeline

Reference 59

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VINE: Taming Generative Control Policies for Reinforcement Learning Efficient Adjoint Matching for Fine-tuning Diffusion Models

Reference 60

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VINE: Taming Generative Control Policies for Reinforcement Learning From Imitation to Refinement -- Residual RL for Precise Assembly

Reference 61

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This paper cites Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model.

VINE: Taming Generative Control Policies for Reinforcement Learning Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model

Reference 62

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 63

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VINE: Taming Generative Control Policies for Reinforcement Learning Unresolved cited work

Reference 64

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VINE: Taming Generative Control Policies for Reinforcement Learning DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning

Reference 65

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VINE: Taming Generative Control Policies for Reinforcement Learning Zhang, Z

Reference 66

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VINE: Taming Generative Control Policies for Reinforcement Learning Gaussian Error Linear Units (GELUs)

Reference 67

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Observation 92697b6e-374a-4753-819e-0900fe5eb8ef · outbound

This paper cites Layer Normalization.

VINE: Taming Generative Control Policies for Reinforcement Learning Layer Normalization

Reference 68

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Observation 7b563fe2-8c7e-4de4-bae2-6ca0a6edc460 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

VINE: Taming Generative Control Policies for Reinforcement Learning Adam: A Method for Stochastic Optimization

Reference 69

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