Paper Citation Record · LEDGER

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn

As of 14 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 3 inbound Pith citation observations for arXiv:2506.00592.

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

pith.paper-citation-record.v1
2506.00592 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:07:26.158821Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T04:05:50.160214Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T05:19:45.749504Z

Reference resolution

26 of 26 outbound references displayed

verified exact1
verified fuzzy6
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ffcaccea-3f58-4563-841a-8a9cd5a74986 · outbound

This paper cites an unresolved cited work.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:07:26.720173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b5fd2619-5719-453a-b739-20d862b81eea · outbound

This paper cites Maintaining Plasticity in Deep Continual Learning.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Maintaining Plasticity in Deep Continual Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:24.694086Z

Source-reported events for the cited work

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Observation 23362e2a-c199-4b18-a865-c8c6fecf0b92 · outbound

This paper cites Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:24.760896Z

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Observation db57ee08-4494-48de-9065-11345ab9a021 · outbound

This paper cites Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:24.841166Z

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Observation 48e55a01-91f5-41f9-ab6d-9dd2ebb39426 · outbound

This paper cites an unresolved cited work.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:07:28.016634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1c4802a7-b563-4996-b8b4-12d37db6f373 · outbound

This paper cites Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:07:27.833015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d5a79819-a857-405e-9ff8-3747ae614d44 · outbound

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

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Disentangling the Causes of Plasticity Loss in Neural Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:25.099245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbe77174-476f-4664-a2db-db10933858d9 · outbound

This paper cites Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:25.165280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed7c0b0f-544b-4b8a-bc29-ae11cb31f517 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Proximal Policy Optimization Algorithms

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:25.392028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16b43741-5e71-4deb-b037-c7410b7eb4ad · outbound

This paper cites DeepMind Control Suite.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn DeepMind Control Suite

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:25.491932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b56d979-6340-4f05-80d1-27ccb30156dc · outbound

This paper cites Deep Reinforcement Learning and the Deadly Triad.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Deep Reinforcement Learning and the Deadly Triad

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:25.603506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 456c8a55-bdc7-4a0e-aca0-dcc8c5803e59 · outbound

This paper cites Continual Learning for Large Language Models: A Survey.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Continual Learning for Large Language Models: A Survey

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:25.660754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d8cfb7f-872f-499e-a4b3-67211ff87b2d · outbound

This paper cites MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:25.752483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7bb5d2b-2af0-440f-9716-0454207ceab0 · outbound

This paper cites Experimental Details A.1.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Experimental Details A.1

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:07:27.584006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9706fc3c-9988-4285-a5b3-3964fe89aa71 · outbound

This paper cites Therefore, we useσ= 0.02for MountainCar-v0.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Therefore, we useσ= 0.02for MountainCar-v0

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:07:27.419347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5ef0ff6e-5ef5-4654-a8e1-83b8d66dd2d6 · outbound

This paper cites The values of conventional hyperparameters are taken from the recommended values inCleanRL.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn The values of conventional hyperparameters are taken from the recommended values inCleanRL

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:07:27.232688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4f1d25d3-7542-4daa-9e50-6e8bc22be371 · outbound

This paper cites The values of conventional hyperpa- rameters are taken from the recommended values in (Young & Tian, 2019).

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn The values of conventional hyperpa- rameters are taken from the recommended values in (Young & Tian, 2019)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:07:27.029517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9c52f6e0-8f13-4e70-9bf1-6672d71ced88 · outbound

This paper cites an unresolved cited work.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:07:26.880531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5a6f8cff-d641-494b-80b6-54bb3dc2d75a · outbound

This paper cites URL http://www.amazon.com/exec/obidos/ redirect?tag=citeulike07-20&path= ASIN/1449319793.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn URL http://www.amazon.com/exec/obidos/ redirect?tag=citeulike07-20&path= ASIN/1449319793

Reference 2013

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Source-reported events for the cited work

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Observation 3d7a8ee3-9fff-4ca4-a75a-0cfaa8c849e6 · outbound

This paper cites Kumar, A., Agarwal, R., Ghosh, D., and Levine, S.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Kumar, A., Agarwal, R., Ghosh, D., and Levine, S

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:24.948826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 253ee046-0a55-4926-aabd-4d8766f4e933 · outbound

This paper cites The Phenomenon of Policy Churn.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn The Phenomenon of Policy Churn

Reference 2019

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:07:26.446753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c49b059f-2ede-4824-b1ea-b1fc61245a3d · outbound

This paper cites A study on the plasticity of neural networks.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn A study on the plasticity of neural networks

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:24.494023Z

Source-reported events for the cited work

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Observation 9bb40a89-7900-44ad-a453-fde6ae77c769 · outbound

This paper cites OpenAI Gym.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn OpenAI Gym

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:24.549328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f76e2d38-6922-4e82-b427-0bb18df945f4 · outbound

This paper cites Continual Learning as Computationally Constrained Reinforcement Learning.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Continual Learning as Computationally Constrained Reinforcement Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:25.020778Z

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Unavailable: canonical work link unavailable.

Observation 97b49dac-aa77-4af6-b424-75d914f9fa9e · outbound

This paper cites Towards Characterizing Divergence in Deep Q-Learning.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Towards Characterizing Divergence in Deep Q-Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T12:07:24.428368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85313622-e286-4119-bcbd-195fa7782460 · outbound

This paper cites Lever- aging procedural generation to benchmark reinforcement learning.

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn Lever- aging procedural generation to benchmark reinforcement learning

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:07:28.195071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 1296f4cd-21ea-4efc-b362-ee2d8912cd13 · inbound

Learning, Fast and Slow: Towards LLMs That Adapt Continually cites this paper.

Learning, Fast and Slow: Towards LLMs That Adapt Continually Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:07:18.519778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 11e621f7-d694-4e05-a863-04a03e4ad03a · inbound

Learning, Fast and Slow: Towards LLMs That Adapt Continually cites this paper.

Learning, Fast and Slow: Towards LLMs That Adapt Continually Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:19:45.752173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0153ae16-e631-4f4e-af06-b8b37d5699ba · inbound

Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning cites this paper.

Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-07-31T04:05:50.160214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.