OGPO: Sample Efficient Full-Finetuning of Generative Control Policies | Paper Citation Record

Observation 6ac0b1b8-3d1f-4090-841c-1f42b4993154 · outbound

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

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 1

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Observation e96d7d8c-10ea-4970-aa9e-90730a5b9759 · outbound

This paper cites Learning long-term dependencies with gradient descent is difficult.IEEE Transactions on Neural Networks, 5(2):157–166.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Learning long-term dependencies with gradient descent is difficult.IEEE Transactions on Neural Networks, 5(2):157–166

Reference 2

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Observation 76562e94-ed6e-4e04-9278-9e2fe6457c2a · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Training Diffusion Models with Reinforcement Learning

Reference 3

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Observation 22b44d03-983e-4d85-bc96-bb9f0b07a5bc · outbound

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

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 4

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Observation 8d89a0db-29e9-447b-acf6-4dc613ddc45c · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.681731Z

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Observation 2c6efab4-06a8-4f45-a19c-dc765a6f0c1a · outbound

This paper cites Randomized Ensembled Double Q-Learning: Learning Fast Without a Model.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

Reference 6

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Observation f24fe5e6-d9d2-48d8-a029-14f0bc103e8c · outbound

This paper cites Diffusion Policy: Visuomotor Policy Learning via Action Diffusion.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Reference 7

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Observation ec093a9a-b7b7-49a3-a3ab-a27d39b7e0b2 · outbound

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

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies EXPO: Stable Reinforcement Learning with Expressive Policies

Reference 8

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Observation c400599a-47be-4c03-b5fc-8696aad2b151 · outbound

This paper cites IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

Reference 9

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Observation 718720ac-6e24-4d11-9a34-0e915dc477d5 · outbound

This paper cites Implicit reparameterization gradients.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Implicit reparameterization gradients

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.366090Z

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Observation 1fa0c259-8845-4328-bd98-786bfbffb5aa · outbound

This paper cites One Step Diffusion via Shortcut Models.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies One Step Diffusion via Shortcut Models

Reference 11

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Observation 7f2b5ac5-f29a-402d-86da-8695f7888a87 · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 12

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Observation 826bc7b6-a777-46cd-8bec-b6b2a8ac091f · outbound

This paper cites Addressing function approximation error in actor-critic methods.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Addressing function approximation error in actor-critic methods

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.369650Z

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Observation 242da48d-3abb-4790-885d-79a84a51b9fd · outbound

This paper cites Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning

Reference 14

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Observation 9b1a8f0b-ebef-4659-bae9-c20cdb464f3a · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.363407Z

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Observation 1758a274-10c2-421a-84d9-a9a2f6b67ff3 · outbound

This paper cites Denoising diffusion probabilistic models.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Denoising diffusion probabilistic models

Reference 16

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Observation e3f67899-95f2-467a-8963-69b9da1675cc · outbound

This paper cites Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization

Reference 17

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Observation 3533448f-c5f0-4446-b417-5e2ad50e496f · outbound

This paper cites $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Reference 18

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Observation f07f14f8-a433-4c7b-85e9-fe3c158a7592 · outbound

This paper cites Auto-Encoding Variational Bayes.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Auto-Encoding Variational Bayes

Reference 19

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Observation 20ee65ab-e992-42e3-8906-0af0ad9a33db · outbound

This paper cites Rl-100: Performant robotic manipulation with real-world reinforcement learning.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Rl-100: Performant robotic manipulation with real-world reinforcement learning

Reference 20

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Observation ac7c232e-910f-48e8-b43d-f7cc059fd905 · outbound

This paper cites Reinforcement Learning with Action Chunking.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Reinforcement Learning with Action Chunking

Reference 21

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Observation d1670ae1-8dc0-4b81-9e93-0f62e2754523 · outbound

This paper cites Flow Matching for Generative Modeling.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Flow Matching for Generative Modeling

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.710124Z

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Observation 875e7b2c-640a-4a2e-8ceb-1985281c6e68 · outbound

This paper cites Libero: Benchmarking knowledge transfer for lifelong robot learning.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Libero: Benchmarking knowledge transfer for lifelong robot learning

Reference 23

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Observation 4cd68232-5d9f-45f6-bc62-2fee9011ed3b · outbound

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

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Flow-GRPO: Training Flow Matching Models via Online RL

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.659427Z

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Observation 25748936-99cd-4c1e-9a20-cd462e37b2ca · outbound

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

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.664320Z

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Observation 51d81c37-bc1c-4839-b742-29f641082b59 · outbound

This paper cites What Matters in Learning from Offline Human Demonstrations for Robot Manipulation.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies What Matters in Learning from Offline Human Demonstrations for Robot Manipulation

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.671629Z

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Observation c6b3ca7a-5ffb-44a1-9f66-0bf9626af0c8 · outbound

This paper cites Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone

Reference 27

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Observation d1f51504-4b4f-4ca0-9bee-40f1946ef096 · outbound

This paper cites Flow Matching Policy Gradients.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Flow Matching Policy Gradients

Reference 28

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Observation 61bda8c9-cd75-41ab-89db-a16147a5a301 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.686495Z

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Observation f28012cd-88ca-40cc-a7a6-200d7b3d585a · outbound

This paper cites Steering your generalists: Improving robotic foundation models via value guidance.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Steering your generalists: Improving robotic foundation models via value guidance

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.362708Z

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Observation 82bbdec9-a19b-4bc5-ac61-840847a6ec38 · outbound

This paper cites Self-imitation learning.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Self-imitation learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.366900Z

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Observation 96f5956d-98bf-4472-abc2-40c0ee4b1942 · outbound

This paper cites Training language models to follow instructions with human feedback.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Training language models to follow instructions with human feedback

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.355488Z

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Observation 74ca1875-142c-4ee6-9ada-bfd5271e0c0c · outbound

This paper cites Much ado about noising: Dispelling the myths of gener- ative robotic control.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Much ado about noising: Dispelling the myths of gener- ative robotic control

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:05:09.654833Z

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Observation 05fabe9c-601c-4697-9543-92f0a4db82a2 · outbound

This paper cites Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Reference 34

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Observation cc0b41cb-7127-422d-a17a-d2688821a6a0 · outbound

This paper cites Film: Visual reasoning with a general conditioning layer.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Film: Visual reasoning with a general conditioning layer

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.357630Z

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Observation 0fb987d5-a3af-463b-96b2-97cc4f8c2ef1 · outbound

This paper cites FAST: Efficient Action Tokenization for Vision-Language-Action Models.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies FAST: Efficient Action Tokenization for Vision-Language-Action Models

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.691460Z

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Observation f35b40ff-a3e3-4c8a-bb2d-b5bc8413b31c · outbound

This paper cites Information Theory: From Coding to Learning.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Information Theory: From Coding to Learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.355896Z

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Observation 2af59ee1-3ad0-4010-935a-3ed5a7f4f754 · outbound

This paper cites Diffusion Policy Policy Optimization.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Diffusion Policy Policy Optimization

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.649912Z

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Observation e22bb612-fe09-4054-9c4b-2cc9240603dc · outbound

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

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies High-resolution image synthesis with latent diffusion models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.364361Z

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Observation 2f2da0d9-5324-4f28-8621-91f535dd0509 · outbound

This paper cites Trust region policy optimization.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Trust region policy optimization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.358942Z

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Observation 4e406356-796c-47db-a645-4c6fcdf9c760 · outbound

This paper cites Proximal Policy Optimization Algorithms.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Proximal Policy Optimization Algorithms

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.647772Z

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Observation 9de63b67-d0db-426d-ae15-87987fce5a98 · outbound

This paper cites e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies e3: Learning to Explore Enables Extrapolation of Test-Time Compute for LLMs

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:05:09.652333Z

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Observation 1c1afc29-c1c9-4d50-826b-43b5ac824d9c · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.707869Z

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Observation 641428c8-6308-451c-952c-4f35cf1e688b · outbound

This paper cites Denoising Diffusion Implicit Models.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Denoising Diffusion Implicit Models

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.705464Z

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Observation 4da2841a-48e1-41ad-906f-22b7e271b714 · outbound

This paper cites Consistency models.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Consistency models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.357242Z

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Observation 5d78a724-dce9-4cc4-8e7b-f394573bde90 · outbound

This paper cites Do differentiable simulators give better policy gradients? In International Conference on Machine Learning, pages 20668--20696.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Do differentiable simulators give better policy gradients? In International Conference on Machine Learning, pages 20668--20696

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.347971Z

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Observation 2b5dd790-ece3-4e16-a3d3-9ba8769b83a4 · outbound

This paper cites Jump-start reinforcement learning.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Jump-start reinforcement learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.368553Z

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Observation a3361abc-5bee-4405-a918-7a7159f73ee3 · outbound

This paper cites Steering Your Diffusion Policy with Latent Space Reinforcement Learning.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Steering Your Diffusion Policy with Latent Space Reinforcement Learning

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.640722Z

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Observation 9b275baa-22b5-4e01-9924-3de4269dfa18 · outbound

This paper cites Bridgedata v2: A dataset for robot learning at scale.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Bridgedata v2: A dataset for robot learning at scale

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.351717Z

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Observation b0014d3f-484a-48b6-8972-c028d1cd7f9e · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforcement learning.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Simple statistical gradient-following algorithms for connectionist reinforcement learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.370461Z

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Observation 64634089-8a90-4fd4-9cc1-cbc55417b948 · outbound

This paper cites Diffusion models for robotic manipulation: A survey.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Diffusion models for robotic manipulation: A survey

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T09:33:36.351866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 672855d0-1ae7-4527-81e8-11987f4ee878 · outbound

This paper cites Multilingual Universal Sentence Encoder for Semantic Retrieval.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Multilingual Universal Sentence Encoder for Semantic Retrieval

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.714639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4124e0b7-f1b1-489f-b42e-b4610a8c1a21 · outbound

This paper cites Flow policy gradients for robot control.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Flow policy gradients for robot control

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:05:09.724331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 975da54b-5f96-4560-b4d1-21dcb172b1fc · outbound

This paper cites Affordance-based robot manipulation with flow matching.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Affordance-based robot manipulation with flow matching

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:05:09.638373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 122ca670-a47d-4927-8753-a54de95dd54c · outbound

This paper cites arXiv preprint arXiv:2507.09061 , year=.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies arXiv preprint arXiv:2507.09061 , year=

Reference 55

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T00:05:09.643171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 77c6309b-ae97-4580-a1c6-82345a315b12 · outbound

This paper cites arXiv preprint arXiv:2505.22094 , year=.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies arXiv preprint arXiv:2505.22094 , year=

Reference 56

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T00:05:09.645589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a10e5ef2-f1fb-4953-b599-84cc3723bbc4 · outbound

This paper cites Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-07-01T00:05:09.635889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5b4de9fb-0cae-4a8d-a72f-9485efdf5270 · outbound

This paper cites Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data.

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-07-01T00:05:09.633737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Outbound references

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