Paper Citation Record · LEDGER

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization

As of 9 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 3 inbound Pith citation observations for arXiv:2502.07523.

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

pith.paper-citation-record.v1
2502.07523 v2

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Pith citing papers itemized under the disclosed page cap.

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

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Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation 75263cad-6299-44ef-8c73-abf37c6bf9d1 · outbound

This paper cites Deep reinforcement learning at the edge of the statistical precipice.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Deep reinforcement learning at the edge of the statistical precipice

Reference 1

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Observation ed9c9d59-5e49-495c-961c-2af5be4e7484 · outbound

This paper cites On Warm-Starting Neural Network Training.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization On Warm-Starting Neural Network Training

Reference 2

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Observation c9f6ac76-8cfe-462c-bf34-87e6816846f2 · outbound

This paper cites Layer Normalization.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Layer Normalization

Reference 3

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This paper cites CrossQ: Batch normalization in deep reinforcement learning for greater sample efficiency and simplicity.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization CrossQ: Batch normalization in deep reinforcement learning for greater sample efficiency and simplicity

Reference 4

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Observation d6659d78-ad05-4bb0-a86d-47dacf7dba7b · outbound

This paper cites Towards Deeper Deep Reinforcement Learning with Spectral Normalization.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Towards Deeper Deep Reinforcement Learning with Spectral Normalization

Reference 5

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This paper cites Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion

Reference 6

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This paper cites MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control

Reference 7

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Observation b7e4f8f6-24f0-4a9f-a7ae-39ec91ea58a0 · outbound

This paper cites Randomized ensembled double Q- learning: Learning fast without a model.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Randomized ensembled double Q- learning: Learning fast without a model

Reference 8

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Observation 352a4d70-af65-4ce7-ada4-5c408307db7a · outbound

This paper cites Sample-efficient reinforcement learning by breaking the replay ratio barrier.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Sample-efficient reinforcement learning by breaking the replay ratio barrier

Reference 9

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Observation b7388153-db91-4c99-bf03-b1b9f5fc9d3b · outbound

This paper cites Weight clipping for deep continual and reinforcement learning.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Weight clipping for deep continual and reinforcement learning

Reference 10

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Observation 124445d9-ca17-48a4-ab62-79762922e9e2 · outbound

This paper cites Jordan, Joseph E.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Jordan, Joseph E

Reference 11

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Observation bc1bb07f-f41d-4ef8-80ef-5542c77c39cf · outbound

This paper cites Sharpness-aware min- imization for efficiently improving generalization.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Sharpness-aware min- imization for efficiently improving generalization

Reference 12

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This paper cites Soft Actor-Critic Algorithms and Applications.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Soft Actor-Critic Algorithms and Applications

Reference 13

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This paper cites Dream to control: Learning behaviors by latent imagination.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Dream to control: Learning behaviors by latent imagination

Reference 14

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Observation ab10e9bd-6f4a-4044-ab68-b771cc5d82d0 · outbound

This paper cites TD-MPC2: Scalable, Robust World Models for Continuous Control.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization TD-MPC2: Scalable, Robust World Models for Continuous Control

Reference 15

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Observation d95cfb06-dee2-455e-815e-27f16afdc36a · outbound

This paper cites Learning continuous control policies by stochastic value gradients.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Learning continuous control policies by stochastic value gradients

Reference 16

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Observation 775bbe21-ce93-4a3f-99e7-d3fe6ff23a9a · outbound

This paper cites Dropout q-functions for doubly efficient reinforcement learning.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Dropout q-functions for doubly efficient reinforcement learning

Reference 17

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Observation 35af7e7d-9a2a-44d5-a273-7c282b4b0a45 · outbound

This paper cites Normalization techniques in training dnns: Methodology, analysis and application.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Normalization techniques in training dnns: Methodology, analysis and application

Reference 18

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Observation 9a1b4d42-adff-45e9-8b04-bdba794b3256 · outbound

This paper cites Dissecting Deep RL with High Update Ratios: Combatting Value Divergence.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

Reference 19

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This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 20

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This paper cites When to trust your model: Model-based policy optimization.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization When to trust your model: Model-based policy optimization

Reference 21

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Observation d7cbeaed-d115-4233-8cca-5a863f876ad7 · outbound

This paper cites Deepmellow: Remov- ing the need for a target network in deep q-learning.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Deepmellow: Remov- ing the need for a target network in deep q-learning

Reference 22

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Observation 582992de-d84e-4821-a9eb-6b892d79df62 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Adam: A Method for Stochastic Optimization

Reference 23

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This paper cites JAXRL: Implementations of Reinforcement Learning algorithms in JAX, 2021.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization JAXRL: Implementations of Reinforcement Learning algorithms in JAX, 2021

Reference 24

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Observation 0518ed5d-c1aa-4997-a5c8-434f8fe814ff · outbound

This paper cites PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning

Reference 25

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Observation 615629a0-8983-4261-a4d4-9821c9b73eb1 · outbound

This paper cites Simba: Simplicity bias for scaling up parameters in deep reinforcement learning.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Simba: Simplicity bias for scaling up parameters in deep reinforcement learning

Reference 26

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Observation 9b1a5874-9527-46ec-95fa-62312d3922b7 · outbound

This paper cites Efficient deep reinforcement learning requires regulating overfitting.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Efficient deep reinforcement learning requires regulating overfitting

Reference 27

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Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Decoupled Weight Decay Regularization

Reference 28

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Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Normalization and effective learning rates in reinforcement learning

Reference 29

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Observation 9320d514-4261-4ba8-abd5-d14d6e858fb2 · outbound

This paper cites Grokking deep reinforcement learning.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Grokking deep reinforcement learning

Reference 30

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This paper cites Bigger, regularized, optimistic: scaling for compute and sample-efficient continuous control.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Bigger, regularized, optimistic: scaling for compute and sample-efficient continuous control

Reference 31

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Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization The primacy bias in deep reinforcement learning

Reference 32

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This paper cites Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

Reference 33

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Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Markov decision processes: discrete stochastic dynamic programming

Reference 34

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This paper cites Weight normalization: A simple reparameterization to accelerate training of deep neural networks.Advances in Neural Information Processing Systems (NeurIPS), 2016.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Weight normalization: A simple reparameterization to accelerate training of deep neural networks.Advances in Neural Information Processing Systems (NeurIPS), 2016

Reference 35

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This paper cites Bigger, better, faster: Human-level atari with human-level efficiency,.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Bigger, better, faster: Human-level atari with human-level efficiency,

Reference 36

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Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units

Reference 37

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Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Integrated architectures for learning, planning, and reacting based on approximating dynamic programming

Reference 38

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Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Sutton and Andrew G

Reference 39

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Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization DeepMind Control Suite

Reference 40

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Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Mujoco: A physics engine for model-based control

Reference 41

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This paper cites When to use parametric models in reinforcement learning? In Advances in Neural Information Processing Systems, 2019.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization When to use parametric models in reinforcement learning? In Advances in Neural Information Processing Systems, 2019

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:32:20.765022Z

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Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization L2 Regularization versus Batch and Weight Normalization

Reference 43

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Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL

Reference 44

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This paper cites Root mean square layer normalization.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Root mean square layer normalization

Reference 45

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This paper cites Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 47

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Observation 714c4790-4185-46eb-9328-c49b541c7c85 · outbound

This paper cites Limitations.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Limitations

Reference 48

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This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 49

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This paper cites To aid reproducibility, we plan to release the code together with the camera-ready version of the paper.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization To aid reproducibility, we plan to release the code together with the camera-ready version of the paper

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:32:20.697899Z

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Observation b674ce1f-a0e4-425e-9a69-476838c88c3c · outbound

This paper cites We plan to release the code together with the publication of the paper.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization We plan to release the code together with the publication of the paper

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:32:20.683917Z

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This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Guidelines: • The answer NA means that the paper does not include experiments

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:32:20.670334Z

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Observation 79634b99-c3fb-4dd3-bd36-d4f912570c3e · outbound

This paper cites Results are aggregated over multiple environments and 10 seeds each.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Results are aggregated over multiple environments and 10 seeds each

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:32:20.656920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 81b417fa-0671-4dc4-aaa7-ce5973b1c6a6 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Guidelines: • The answer NA means that the paper does not include experiments

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:32:20.642686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b74d57b4-a312-4725-91d9-032675b7d9cb · outbound

This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:32:20.627524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 73ed4cfe-9dba-41ea-8e2c-66046cbf7835 · outbound

This paper cites As actor-critic methods already enjoy a long history, there is no additional societal impact with this research contribution.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization As actor-critic methods already enjoy a long history, there is no additional societal impact with this research contribution

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:32:20.613570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fbd4819c-cde6-425b-a14a-2103608adc12 · outbound

This paper cites an unresolved cited work.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:32:20.599457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1cc4494b-c4ec-49df-94cb-8727302ce406 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not use existing assets.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Guidelines: • The answer NA means that the paper does not use existing assets

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:32:20.585362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1659f625-38a3-477a-8cb6-ec380efaf8b4 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not release new assets.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Guidelines: • The answer NA means that the paper does not release new assets

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:32:20.571135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 58009948-31b4-4bdf-81cf-8cdde5f88573 · outbound

This paper cites an unresolved cited work.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Unresolved cited work

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-08T12:32:20.208772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c05b1cf6-fef2-4e2e-807c-e4aa83e6e252 · outbound

This paper cites • Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization • Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:32:20.548252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7a401270-6cba-409f-870d-c645450b258a · outbound

This paper cites an unresolved cited work.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:32:20.533194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f2f1f8d1-f3ba-4ee3-a3e9-b0a4e6212f51 · outbound

This paper cites Bigger, Better, Faster: Human-level Atari with human-level efficiency.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Bigger, Better, Faster: Human-level Atari with human-level efficiency

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T12:32:20.107232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 47bb1096-30d7-43a5-a96a-02dbf597181b · inbound

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies cites this paper.

Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T04:39:06.014406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e5df350-6c5b-40a5-8584-b9664a6ecbbb · inbound

Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning cites this paper.

Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:46:27.863900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a3db05dd-9295-402a-bc36-ca5c22c7c0fe · inbound

Extending Differential Temporal Difference Methods for Episodic Problems cites this paper.

Extending Differential Temporal Difference Methods for Episodic Problems Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:35:38.842202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.