Paper Citation Record · LEDGER

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning

As of 21 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2507.14736.

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

pith.paper-citation-record.v1
2507.14736 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:55:37.415084Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T13:00:27.749673Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T13:01:23.593607Z

Reference resolution

40 of 40 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1529e61d-3d56-4248-8900-dcff23ca077b · outbound

This paper cites write newline.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning write newline

Reference 1

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Observation 3ee09e3f-1378-462f-a12d-9b82fc8fbe43 · outbound

This paper cites Layer Normalization.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Layer Normalization

Reference 2

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Observation adc39da1-4eab-472f-9971-8e69aba96167 · outbound

This paper cites Ball, Laura M.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Ball, Laura M

Reference 3

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Observation 35576cc5-837a-4b8a-9879-c25f2c00a829 · outbound

This paper cites Bellemare, Yavar Naddaf, J.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Bellemare, Yavar Naddaf, J

Reference 4

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This paper cites Adaptive rational activations to boost deep reinforcement learning.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Adaptive rational activations to boost deep reinforcement learning

Reference 5

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

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Sample-efficient reinforcement learning by breaking the replay ratio barrier

Reference 6

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Observation 558e5c0e-9253-43d7-8bf9-fa01c162a910 · outbound

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

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Sample-efficient reinforcement learning by breaking the replay ratio barrier

Reference 7

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Observation 1863f504-86c8-4d0a-96ed-2e2f4b653f6e · outbound

This paper cites Abbeel, and S.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Abbeel, and S

Reference 8

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This paper cites Rainbow: Combining improvements in deep reinforcement learning.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Rainbow: Combining improvements in deep reinforcement learning

Reference 9

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This paper cites Neural tangent kernel: Convergence and generalization in neural networks.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Neural tangent kernel: Convergence and generalization in neural networks

Reference 10

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Observation cef2994e-e1ef-44d0-bf1b-526390a46469 · outbound

This paper cites Maintaining Plasticity in Continual Learning via Regenerative Regularization.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 11

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Observation 4bfbcbc3-15a9-4769-8931-49954e548b15 · outbound

This paper cites SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

Reference 12

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Unresolved cited work

Reference 13

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Unresolved cited work

Reference 14

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Unresolved cited work

Reference 15

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This paper cites Functional regularization for reinforcement learning via learned fourier features.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Functional regularization for reinforcement learning via learned fourier features

Reference 16

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Observation a9c8ffdb-9a6b-41e7-a2b5-8bf457f1ff85 · outbound

This paper cites Understanding and preventing capacity loss in reinforcement learning.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Understanding and preventing capacity loss in reinforcement learning

Reference 17

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Observation d09ab8c8-6136-47d6-929a-36f50f2c3ce4 · outbound

This paper cites Understanding plasticity in neural networks.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Understanding plasticity in neural networks

Reference 18

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This paper cites Understanding plasticity in neural networks.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Understanding plasticity in neural networks

Reference 19

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This paper cites Frequency and Generalisation of Periodic Activation Functions in Reinforcement Learning.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Frequency and Generalisation of Periodic Activation Functions in Reinforcement Learning

Reference 20

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This paper cites Playing Atari with Deep Reinforcement Learning.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Playing Atari with Deep Reinforcement Learning

Reference 21

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

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Human-level control through deep reinforcement learning

Reference 22

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This paper cites Pad \' e activation units: End-to-end learning of flexible activation functions in deep networks.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Pad \' e activation units: End-to-end learning of flexible activation functions in deep networks

Reference 23

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Pad\'e Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks

Reference 24

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This paper cites Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning

Reference 25

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

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 26

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Courville

Reference 27

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning On the difficulty of training recurrent neural networks

Reference 28

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Data-Efficient Reinforcement Learning with Self-Predictive Representations

Reference 29

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Courville, Marc G

Reference 30

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This paper cites The Dormant Neuron Phenomenon in Deep Reinforcement Learning.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning The Dormant Neuron Phenomenon in Deep Reinforcement Learning

Reference 31

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning The dormant neuron phenomenon in deep reinforcement learning

Reference 32

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Srinivasan, B

Reference 33

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning DeepMind Control Suite

Reference 35

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Overcoming the spectral bias of neural value approximation

Reference 36

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This paper cites Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning.

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning

Reference 37

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 38

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning @esa (Ref

Reference 39

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Unresolved cited work

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Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning Unresolved cited work

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Pith citing papers

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Activation Function Design Sustains Plasticity in Continual Learning cites this paper.

Activation Function Design Sustains Plasticity in Continual Learning Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning

Reference 24

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