Paper Citation Record · LEDGER

Value-Based Deep RL Scales Predictably

As of 19 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 5 inbound Pith citation observations for arXiv:2502.04327.

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

pith.paper-citation-record.v1
2502.04327 v2

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:50:34.155640Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:58:52.599976Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T23:09:12.714863Z

Reference resolution

61 of 61 outbound references displayed

verified exact0
verified fuzzy49
unresolved12
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a94a2006-4cda-4f14-981a-d5d2dda951c6 · outbound

This paper cites write newline.

Value-Based Deep RL Scales Predictably write newline

Reference 1

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Observation ab0650aa-07fb-499b-9796-cde2ef210b24 · outbound

This paper cites GPT -4 technical report.

Value-Based Deep RL Scales Predictably GPT -4 technical report

Reference 2

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Observation 46769471-4517-4647-ad00-03ab6025acd0 · outbound

This paper cites The isotonic regression problem and its dual.

Value-Based Deep RL Scales Predictably The isotonic regression problem and its dual

Reference 3

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Observation d089a7e0-efd8-4d33-834b-0c01e5af1a3a · outbound

This paper cites Pattern Recognition and Machine Learning.

Value-Based Deep RL Scales Predictably Pattern Recognition and Machine Learning

Reference 4

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Observation 2a04ab3d-9f6b-422c-a0dc-1c5360bd21ee · outbound

This paper cites OpenAI Gym , 2016.

Value-Based Deep RL Scales Predictably OpenAI Gym , 2016

Reference 5

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Observation 7eb90764-31b0-40c4-b454-096967c9bbc5 · outbound

This paper cites Video generation models as world simulators.

Value-Based Deep RL Scales Predictably Video generation models as world simulators

Reference 6

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Observation 852f239f-9b6c-4765-8c63-6b52b164378d · outbound

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

Value-Based Deep RL Scales Predictably Randomized ensembled double Q -learning: Learning fast without a model

Reference 7

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Observation 60159157-8014-4dbf-88d1-249abf341791 · outbound

This paper cites The value-improvement path: Towards better representations for reinforcement learning.

Value-Based Deep RL Scales Predictably The value-improvement path: Towards better representations for reinforcement learning

Reference 8

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Observation 3f4817d9-e105-4c53-9a66-0ca15e87e663 · outbound

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

Value-Based Deep RL Scales Predictably Sample-efficient reinforcement learning by breaking the replay ratio barrier

Reference 9

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Observation b2c7db4c-7743-4e74-9ef8-232c268208c6 · outbound

This paper cites The Llama 3 herd of models.

Value-Based Deep RL Scales Predictably The Llama 3 herd of models

Reference 10

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Observation 8cc2861c-5e2b-4a33-b119-9338a2ee829a · outbound

This paper cites IMPALA : Scalable distributed deep- RL with importance weighted actor-learner architectures.

Value-Based Deep RL Scales Predictably IMPALA : Scalable distributed deep- RL with importance weighted actor-learner architectures

Reference 11

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Observation d5250699-4756-407d-8bbb-dc4baada40de · outbound

This paper cites Language models scale reliably with over-training and on downstream tasks.

Value-Based Deep RL Scales Predictably Language models scale reliably with over-training and on downstream tasks

Reference 12

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Observation 8ccf7e35-19a6-4457-8bca-a0d23914458b · outbound

This paper cites Simplifying deep temporal difference learning.

Value-Based Deep RL Scales Predictably Simplifying deep temporal difference learning

Reference 13

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Observation 65dda2fc-cb8a-4df9-831e-6e5e1a2a50b8 · outbound

This paper cites Scaling laws for reward model overoptimization.

Value-Based Deep RL Scales Predictably Scaling laws for reward model overoptimization

Reference 14

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Observation 6868a284-8f1c-40c2-b8f4-abef9ae465be · outbound

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

Value-Based Deep RL Scales Predictably Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 15

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Observation a9aabe5d-25c1-4a34-a9d0-7b6e22123557 · outbound

This paper cites Mastering diverse domains through world models.

Value-Based Deep RL Scales Predictably Mastering diverse domains through world models

Reference 16

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Observation f93fd152-ee35-4bb2-8b61-7b5df227e8e4 · outbound

This paper cites Scaling laws for single-agent reinforcement learning.

Value-Based Deep RL Scales Predictably Scaling laws for single-agent reinforcement learning

Reference 17

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Observation 38c12941-3766-4884-abc6-5dd8d4ade8a0 · outbound

This paper cites Training compute-optimal large language models.

Value-Based Deep RL Scales Predictably Training compute-optimal large language models

Reference 18

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Observation d9a6a78b-3850-451b-a8f8-8d996de70a2f · outbound

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

Value-Based Deep RL Scales Predictably When to trust your model: Model-based policy optimization

Reference 19

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Observation 445f83e8-9890-4e60-9939-858eafe69d29 · outbound

This paper cites an unresolved cited work.

Value-Based Deep RL Scales Predictably Unresolved cited work

Reference 20

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This paper cites Scaling laws for neural language models.

Value-Based Deep RL Scales Predictably Scaling laws for neural language models

Reference 21

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Observation 4318d582-b797-4984-95ff-7d4fa607f897 · outbound

This paper cites One weird trick for parallelizing convolutional neural networks.

Value-Based Deep RL Scales Predictably One weird trick for parallelizing convolutional neural networks

Reference 22

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Observation 45af0646-657e-42e6-81ce-8e5607ea19d4 · outbound

This paper cites Implicit under-parameterization inhibits data-efficient deep reinforcement learning.

Value-Based Deep RL Scales Predictably Implicit under-parameterization inhibits data-efficient deep reinforcement learning

Reference 23

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Observation 6d8cd011-d773-4ed7-a2bf-4ba6b7b85091 · outbound

This paper cites DR3 : Value-based deep reinforcement learning requires explicit regularization.

Value-Based Deep RL Scales Predictably DR3 : Value-based deep reinforcement learning requires explicit regularization

Reference 24

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Observation 2d7d541c-d0e3-4144-b23a-b5ac511ae60d · outbound

This paper cites Offline Q -learning on diverse multi-task data both scales and generalizes.

Value-Based Deep RL Scales Predictably Offline Q -learning on diverse multi-task data both scales and generalizes

Reference 25

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Observation a77f9a6a-8821-42b1-8285-193d73da3323 · outbound

This paper cites Plastic: Improving input and label plasticity for sample efficient reinforcement learning.

Value-Based Deep RL Scales Predictably Plastic: Improving input and label plasticity for sample efficient reinforcement learning

Reference 26

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Observation 42100522-0b73-4742-a361-b3e43167bb73 · outbound

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

Value-Based Deep RL Scales Predictably SimBa : Simplicity bias for scaling up parameters in deep reinforcement learning

Reference 27

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This paper cites Offline reinforcement learning: Tutorial, review, and perspectives on open problems.

Value-Based Deep RL Scales Predictably Offline reinforcement learning: Tutorial, review, and perspectives on open problems

Reference 28

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This paper cites Efficient deep reinforcement learning requires regulating overfitting.

Value-Based Deep RL Scales Predictably Efficient deep reinforcement learning requires regulating overfitting

Reference 29

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This paper cites Parallel Q -learning: Scaling off-policy reinforcement learning under massively parallel simulation.

Value-Based Deep RL Scales Predictably Parallel Q -learning: Scaling off-policy reinforcement learning under massively parallel simulation

Reference 30

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Observation da99da43-bab3-4570-aadd-b9f5ac7bc6b6 · outbound

This paper cites Continuous control with deep reinforcement learning.

Value-Based Deep RL Scales Predictably Continuous control with deep reinforcement learning

Reference 31

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Observation 84f79e94-a75d-49cf-b86f-7c976fd631aa · outbound

This paper cites Scaling laws for fine-grained mixture of experts.

Value-Based Deep RL Scales Predictably Scaling laws for fine-grained mixture of experts

Reference 32

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Observation 95c8a962-f99a-4380-b627-72f3c86d5438 · outbound

This paper cites Understanding plasticity in neural networks.

Value-Based Deep RL Scales Predictably Understanding plasticity in neural networks

Reference 33

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This paper cites Isaac Gym : High performance GPU -based physics simulation for robot learning.

Value-Based Deep RL Scales Predictably Isaac Gym : High performance GPU -based physics simulation for robot learning

Reference 34

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Value-Based Deep RL Scales Predictably An empirical model of large-batch training

Reference 35

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This paper cites Human-level control through deep reinforcement learning.

Value-Based Deep RL Scales Predictably Human-level control through deep reinforcement learning

Reference 36

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Value-Based Deep RL Scales Predictably Asynchronous methods for deep reinforcement learning

Reference 37

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This paper cites Scaling data-constrained language models.

Value-Based Deep RL Scales Predictably Scaling data-constrained language models

Reference 38

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This paper cites Overestimation, overfitting, and plasticity in actor-critic: The bitter lesson of reinforcement learning.

Value-Based Deep RL Scales Predictably Overestimation, overfitting, and plasticity in actor-critic: The bitter lesson of reinforcement learning

Reference 39

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

Value-Based Deep RL Scales Predictably Bigger, regularized, optimistic: Scaling for compute and sample-efficient continuous control

Reference 40

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This paper cites The primacy bias in deep reinforcement learning.

Value-Based Deep RL Scales Predictably The primacy bias in deep reinforcement learning

Reference 41

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This paper cites Is value learning really the main bottleneck in offline RL ? Advances in Neural Information Processing Systems, 2024.

Value-Based Deep RL Scales Predictably Is value learning really the main bottleneck in offline RL ? Advances in Neural Information Processing Systems, 2024

Reference 42

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This paper cites Hierarchical text-conditional image generation with CLIP latents.

Value-Based Deep RL Scales Predictably Hierarchical text-conditional image generation with CLIP latents

Reference 43

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Observation 2e36fc28-0661-4ad9-bcd8-81ada1a772bb · outbound

This paper cites Mastering Atari , Go , chess and Shogi by planning with a learned model.

Value-Based Deep RL Scales Predictably Mastering Atari , Go , chess and Shogi by planning with a learned model

Reference 44

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Observation 9f4e15e0-c640-431a-9fb8-700b030b5344 · outbound

This paper cites Proximal policy optimization algorithms.

Value-Based Deep RL Scales Predictably Proximal policy optimization algorithms

Reference 45

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

Value-Based Deep RL Scales Predictably Bigger, better, faster: Human-level Atari with human-level efficiency

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:50:34.302091Z

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Observation 5a430338-03f3-4ad6-b4c5-a34cd3733eec · outbound

This paper cites Mastering the game of Go with deep neural networks and tree search.

Value-Based Deep RL Scales Predictably Mastering the game of Go with deep neural networks and tree search

Reference 47

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Source-reported events for the cited work

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This paper cites SAPG : Split and aggregate policy gradients.

Value-Based Deep RL Scales Predictably SAPG : Split and aggregate policy gradients

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:50:34.288447Z

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Observation 13a14c9a-0a6f-42c0-a627-5d31545b7602 · outbound

This paper cites The dormant neuron phenomenon in deep reinforcement learning.

Value-Based Deep RL Scales Predictably The dormant neuron phenomenon in deep reinforcement learning

Reference 49

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Observation d880c06a-a724-438d-887c-f8e3e5ce3455 · outbound

This paper cites Offline actor-critic reinforcement learning scales to large models.

Value-Based Deep RL Scales Predictably Offline actor-critic reinforcement learning scales to large models

Reference 50

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Observation c808fa9a-2d26-4506-a96a-d5b8cf981156 · outbound

This paper cites Reinforcement Learning: An Introduction.

Value-Based Deep RL Scales Predictably Reinforcement Learning: An Introduction

Reference 51

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Observation fde0c6e6-6d02-4df5-af67-dbcdad5eef6b · outbound

This paper cites DeepMind control suite.

Value-Based Deep RL Scales Predictably DeepMind control suite

Reference 52

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Observation 4f5daa81-71cf-47e6-9a55-6a1aa11b9cef · outbound

This paper cites Gemini : A family of highly capable multimodal models.

Value-Based Deep RL Scales Predictably Gemini : A family of highly capable multimodal models

Reference 53

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Observation a61064de-eb67-4b09-9730-3f2f821f42e5 · outbound

This paper cites dm\_control: Software and tasks for continuous control.

Value-Based Deep RL Scales Predictably dm\_control: Software and tasks for continuous control

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:50:34.236045Z

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This paper cites SciPy 1.0: Fundamental algorithms for scientific computing in Python.

Value-Based Deep RL Scales Predictably SciPy 1.0: Fundamental algorithms for scientific computing in Python

Reference 55

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Value-Based Deep RL Scales Predictably DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Reference 56

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This paper cites Tensor programs V : Tuning large neural networks via zero-shot hyperparameter transfer.

Value-Based Deep RL Scales Predictably Tensor programs V : Tuning large neural networks via zero-shot hyperparameter transfer

Reference 57

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Observation 3c1fa771-e5cd-4d0a-979f-d37b620ecc1a · outbound

This paper cites How to leverage unlabeled data in offline reinforcement learning.

Value-Based Deep RL Scales Predictably How to leverage unlabeled data in offline reinforcement learning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:50:34.209081Z

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Observation b2c92c87-3905-4123-b6a1-b0bbf2073a2d · outbound

This paper cites @esa (Ref.

Value-Based Deep RL Scales Predictably @esa (Ref

Reference 59

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Observation a06c7c6a-6d48-47bf-a2db-61889dbf1be1 · outbound

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Value-Based Deep RL Scales Predictably Unresolved cited work

Reference 60

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Observation 5295faeb-a8ea-4c43-854e-c51d9f0ff23c · outbound

This paper cites an unresolved cited work.

Value-Based Deep RL Scales Predictably Unresolved cited work

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-08T22:50:34.155640Z

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Pith citing papers

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Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners cites this paper.

Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners Value-Based Deep RL Scales Predictably

Reference 85

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Observation e7d746db-8c02-4bce-8d81-72aab31f19d0 · 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 Value-Based Deep RL Scales Predictably

Reference 41

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Observation b6387637-3a7d-4c8d-ab63-6aa13395de49 · inbound

When Does Non-Uniform Replay Matter in Reinforcement Learning? cites this paper.

When Does Non-Uniform Replay Matter in Reinforcement Learning? Value-Based Deep RL Scales Predictably

Reference 27

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Observation cadfc5c9-2956-4f39-ade1-1c0075c43668 · inbound

When Does Non-Uniform Replay Matter in Reinforcement Learning? cites this paper.

When Does Non-Uniform Replay Matter in Reinforcement Learning? Value-Based Deep RL Scales Predictably

Reference 27

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Observation 19d61ca9-241b-4520-8130-36a7f28b563c · inbound

When Does Non-Uniform Replay Matter in Reinforcement Learning? cites this paper.

When Does Non-Uniform Replay Matter in Reinforcement Learning? Value-Based Deep RL Scales Predictably

Reference 27

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