Paper Citation Record · LEDGER

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning

As of 18 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 2 inbound Pith citation observations for arXiv:2506.17204.

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

pith.paper-citation-record.v1
2506.17204 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:16:29.086322Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T04:39:32.121584Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T08:16:01.341421Z

Reference resolution

62 of 62 outbound references displayed

verified exact2
verified fuzzy23
unresolved37
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3b216b09-3366-493b-9e34-336a37c8ad25 · outbound

This paper cites write newline.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning write newline

Reference 1

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Observation 50d8c0c6-a8a0-42fb-a460-1b57a6a2b2e7 · outbound

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Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Unresolved cited work

Reference 2

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Observation 147d1fd6-dcdf-41db-9ed7-45ea250aeecd · outbound

This paper cites Single-Shot Pruning for Offline Reinforcement Learning.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Single-Shot Pruning for Offline Reinforcement Learning

Reference 3

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Observation d6e72927-5461-4330-a37d-b4daafeac9ad · outbound

This paper cites Y., Ohib, R., Plis, S.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Y., Ohib, R., Plis, S

Reference 4

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Observation cb36a067-a6f7-4fea-96f6-f8ff120d35a1 · outbound

This paper cites Interference and generalization in temporal difference learning.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Interference and generalization in temporal difference learning

Reference 5

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Observation d5570592-ce6c-4d62-be27-4e9955ddffeb · outbound

This paper cites Simplicity bias in overparameterized machine learning.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Simplicity bias in overparameterized machine learning

Reference 6

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Observation aa5d43b7-0508-40b9-bced-d68668daaa98 · outbound

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

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Crossq: Batch normalization in deep reinforcement learning for greater sample efficiency and simplicity

Reference 7

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Observation f463ff34-6b2a-4ec3-84e6-c0e3cea24643 · outbound

This paper cites P., and Weinberger, K.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning P., and Weinberger, K

Reference 8

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Observation 752eb7dd-06a6-4186-b214-2592dd456890 · outbound

This paper cites Dopamine: A Research Framework for Deep Reinforcement Learning.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Dopamine: A Research Framework for Deep Reinforcement Learning

Reference 9

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Observation 39f40a16-b53f-468a-b444-22659f4280b4 · outbound

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Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Unresolved cited work

Reference 10

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Observation a1e7e449-4a69-4b77-bbaa-8d1284fcd5a2 · outbound

This paper cites an unresolved cited work.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Unresolved cited work

Reference 11

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Observation f8b3072d-bdb6-47e1-aa71-17171333cd7e · outbound

This paper cites Better exploration with optimistic actor critic.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Better exploration with optimistic actor critic

Reference 12

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Observation e56ce9ab-076b-417c-832c-411450ab8b3d · outbound

This paper cites F., Lan, Q., Rahman, P., Mahmood, A.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning F., Lan, Q., Rahman, P., Mahmood, A

Reference 13

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Observation 9df20830-c7e2-4633-b692-e25aba440f85 · outbound

This paper cites Streaming Deep Reinforcement Learning Finally Works.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Streaming Deep Reinforcement Learning Finally Works

Reference 14

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Observation 08393e52-c4a6-4330-a10d-202f8d7015b2 · outbound

This paper cites Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures

Reference 15

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Observation 676b786e-5de7-42c2-a28f-b13f42cb6ee3 · outbound

This paper cites S., and Elsen, E.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning S., and Elsen, E

Reference 16

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Observation 537ae707-827c-4b0e-a790-da1729dde2e8 · outbound

This paper cites Stop Regressing: Training Value Functions via Classification for Scalable Deep RL.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

Reference 17

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Observation b836b606-2936-46e7-962e-ef2eb691bfcd · outbound

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

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Addressing function approximation error in actor-critic methods

Reference 18

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Observation 276456f7-e791-487a-9c6b-f2fd0bf0f1e0 · outbound

This paper cites J., Gu, S.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning J., Gu, S

Reference 19

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Observation 721330c9-be0b-40bf-bf43-596a35d3b977 · outbound

This paper cites Can Learned Optimization Make Reinforcement Learning Less Difficult?.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Can Learned Optimization Make Reinforcement Learning Less Difficult?

Reference 20

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Observation 775e7379-091d-416b-a053-5b9321929d6e · outbound

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Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Unresolved cited work

Reference 21

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Observation 815ddefb-b96f-4ecc-bbef-d7897086f9f5 · outbound

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

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 22

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Observation bedb83d9-d027-4ec8-aece-70dcbe3da1b5 · outbound

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

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning TD-MPC2: Scalable, Robust World Models for Continuous Control

Reference 23

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Observation 5f603a06-759e-4006-bcdd-2c7ff3b86598 · outbound

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

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Rainbow: Combining improvements in deep reinforcement learning

Reference 24

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Observation d7f411cc-ea7c-4429-a171-41902c04034b · outbound

This paper cites N., Liu, S., Marculescu, R., and Wang, Z.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning N., Liu, S., Marculescu, R., and Wang, Z

Reference 25

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Observation 4baadd4a-4168-4ec8-bccb-3c71d7b2dfd3 · outbound

This paper cites A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning

Reference 26

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This paper cites H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., Levine, S., et al.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., Levine, S., et al

Reference 27

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Observation 8d919c62-47cb-47b6-a48f-8bcf3366d87a · outbound

This paper cites Plasticity Loss in Deep Reinforcement Learning: A Survey.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Plasticity Loss in Deep Reinforcement Learning: A Survey

Reference 28

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Observation a4ee0848-9e17-49d1-9162-bf53cd1ce51e · outbound

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

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Implicit under-parameterization inhibits data-efficient deep reinforcement learning

Reference 29

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Observation 26d6f8b7-6fee-47d4-9c1f-d3117364c195 · outbound

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

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Plastic: Improving input and label plasticity for sample efficient reinforcement learning

Reference 30

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Observation f39de609-c80d-4f54-8e23-36ecbf665a62 · outbound

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

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

Reference 31

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This paper cites SNIP : SINGLE - SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning SNIP : SINGLE - SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY

Reference 32

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Observation 07c6204a-73b4-45cf-99f5-5fc4a4574232 · outbound

This paper cites R., and Hinton, G.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning R., and Hinton, G

Reference 33

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Observation 79ae3f09-e7a4-4f9e-8542-1bc5cdbcb4b0 · outbound

This paper cites Directions of Curvature as an Explanation for Loss of Plasticity.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Directions of Curvature as an Explanation for Loss of Plasticity

Reference 34

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This paper cites Continuous control with deep reinforcement learning.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Continuous control with deep reinforcement learning

Reference 35

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Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Neuroplastic Expansion in Deep Reinforcement Learning

Reference 36

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This paper cites C., Wang, Z., and Pechenizkiy, M.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning C., Wang, Z., and Pechenizkiy, M

Reference 37

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Observation 1281e71b-7346-4e43-8328-d90b8e15f071 · outbound

This paper cites Understanding and Preventing Capacity Loss in Reinforcement Learning.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 38

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Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Learning dynamics and generalization in deep reinforcement learning

Reference 39

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This paper cites A., Pascanu, R., and Dabney, W.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning A., Pascanu, R., and Dabney, W

Reference 40

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Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Normalization and effective learning rates in reinforcement learning

Reference 41

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This paper cites Disentangling the Causes of Plasticity Loss in Neural Networks.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Disentangling the Causes of Plasticity Loss in Neural Networks

Reference 42

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This paper cites Revisiting plasticity in visual reinforcement learning: Data, modules and training stages.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Revisiting plasticity in visual reinforcement learning: Data, modules and training stages

Reference 43

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Observation 7b42bb78-1f05-4e80-abbf-6c9cb9a473e8 · outbound

This paper cites C., Mocanu, E., Stone, P., Nguyen, P.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning C., Mocanu, E., Stone, P., Nguyen, P

Reference 44

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

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Overestimation, overfitting, and plasticity in actor-critic: the bitter lesson of reinforcement learning

Reference 45

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Observation 9be0e00a-04f7-4e2a-98cf-dac3917098c0 · outbound

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

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Bigger, regularized, optimistic: scaling for compute and sample-efficient continuous control

Reference 46

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Observation 11754b74-b306-4610-b9cd-5749dc549717 · outbound

This paper cites Parameter, experience, and compute efficient deep reinforcement learning.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Parameter, experience, and compute efficient deep reinforcement learning

Reference 47

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Observation 82639088-f5c0-43af-98eb-7c38014ce0ef · outbound

This paper cites The primacy bias in deep reinforcement learning.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning The primacy bias in deep reinforcement learning

Reference 48

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This paper cites R., Mustafa, B., Renggli, C., Pinto, A.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning R., Mustafa, B., Renggli, C., Pinto, A

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:16:29.394412Z

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This paper cites R., Mustafa, B., and Houlsby, N.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning R., Mustafa, B., and Houlsby, N

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:16:29.383419Z

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Observation a0b70d3c-7a9e-47f4-a03c-e3b6d4dd053c · outbound

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Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Unresolved cited work

Reference 51

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This paper cites The pitfalls of simplicity bias in neural networks.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning The pitfalls of simplicity bias in neural networks

Reference 52

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This paper cites Dynamic Sparse Training for Deep Reinforcement Learning.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Dynamic Sparse Training for Deep Reinforcement Learning

Reference 53

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This paper cites S., and Evci, U.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning S., and Evci, U

Reference 54

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Observation e826e8ef-b828-45e7-8463-d454af47e77c · outbound

This paper cites RL x2: Training a sparse deep reinforcement learning model from scratch.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning RL x2: Training a sparse deep reinforcement learning model from scratch

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:16:29.354425Z

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Observation 884867cb-5336-4f9c-8768-509e25106d40 · outbound

This paper cites DeepMind Control Suite.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning DeepMind Control Suite

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T19:16:29.065972Z

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Observation 3391e03c-38c9-40ba-b113-81723a262680 · outbound

This paper cites Mujoco: A physics engine for model-based control.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Mujoco: A physics engine for model-based control

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T19:16:29.069429Z

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Observation 02283a12-807d-4796-ad91-61d27bb81202 · outbound

This paper cites P., Hessel, M., and Aslanides, J.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning P., Hessel, M., and Aslanides, J

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:16:29.336952Z

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Observation cd65b16c-a140-415e-b947-da1f44889529 · outbound

This paper cites Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T19:16:29.076338Z

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Observation c21b38a0-8521-41a8-80bb-89be0eb21e74 · outbound

This paper cites On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:16:29.132153Z

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Observation 2d0c3140-af5c-45a7-b6ae-725174c82bba · outbound

This paper cites D., Huang, F., and Xu, H.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning D., Huang, F., and Xu, H

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:16:29.323073Z

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Observation b7965002-1921-4f65-9dad-7edc88d56064 · outbound

This paper cites Mastering visual continuous control: Improved data-augmented reinforcement learning.

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning Mastering visual continuous control: Improved data-augmented reinforcement learning

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T19:16:29.086322Z

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Pith citing papers

Observation 58d91c03-d801-48db-854f-db06c394b820 · inbound

Heterogeneous Connectivity in Sparse Networks: Fan-in Profiles, Gradient Hierarchy, and Topological Equilibria cites this paper.

Heterogeneous Connectivity in Sparse Networks: Fan-in Profiles, Gradient Hierarchy, and Topological Equilibria Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:16:01.348346Z

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Observation 2f411e8c-d5e5-409b-8df3-47f3fb835084 · inbound

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback cites this paper.

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning

Reference 244

Resolution: unresolved; no resolver link, observed 2026-08-03T04:39:32.121584Z

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