Paper Citation Record · LEDGER

Understanding and Preventing Capacity Loss in Reinforcement Learning

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2204.09560.

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

pith.paper-citation-record.v1
2204.09560 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:38:06.725532Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T16:57:24.573566Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a1a15da9-0dad-46a7-929d-d6a505e969f1 · inbound

Parseval Regularization for Continual Reinforcement Learning cites this paper.

Parseval Regularization for Continual Reinforcement Learning Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T19:03:05.985909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51158318-6679-420f-aec8-73057e69c61f · inbound

Fisher-Guided Selective Forgetting: Mitigating The Primacy Bias in Deep Reinforcement Learning cites this paper.

Fisher-Guided Selective Forgetting: Mitigating The Primacy Bias in Deep Reinforcement Learning Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T17:46:07.271227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 846107ef-1787-448e-8112-22a7695d7451 · inbound

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss cites this paper.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T15:43:34.553252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b63cdeba-d3e4-4fbd-adfd-fd6d972faf9f · inbound

Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior cites this paper.

Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:42:26.383419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5e9b94d0-a56a-47d3-8527-fbd7b3c0daca · inbound

Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior cites this paper.

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-25T07:55:33.131521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning cites this paper.

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

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T19:16:28.998663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb5bdd2e-32be-40bf-89ce-e8fb9e1f30f0 · inbound

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

Activation Function Design Sustains Plasticity in Continual Learning Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:01:23.603902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9ea34ce9-f781-4f74-8c4a-411656c70a3a · inbound

Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation cites this paper.

Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T20:06:56.079450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c15df262-6541-4e64-949b-fea605e0d0b1 · inbound

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning cites this paper.

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T11:05:08.911436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b2d939fb-97e6-4889-8df8-8cec07136b45 · inbound

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning cites this paper.

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-12T19:46:39.624903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11530a26-4930-4303-840e-f8c25b3d20c0 · inbound

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

Learning, Fast and Slow: Towards LLMs That Adapt Continually Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T05:07:18.507824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2b305cfa-b465-427c-87e6-4308b9231efd · inbound

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

Learning, Fast and Slow: Towards LLMs That Adapt Continually Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T05:19:45.713412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e70bb974-f3a5-44f5-aff8-4f8d3dff1e79 · inbound

Preserving Plasticity in Continual Learning via Dynamical Isometry cites this paper.

Preserving Plasticity in Continual Learning via Dynamical Isometry Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:07:28.518473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 630d04db-a618-4c97-9fe6-479fcbf241e1 · inbound

When Does Continual Learning Require Learning cites this paper.

When Does Continual Learning Require Learning Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 36

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T16:57:24.574723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b15736b1-ef4e-4de5-8fd5-5f056a3e76c6 · inbound

Memory Merge DQN: Sensitivity Weighted Target Updates for Stable Value Learning cites this paper.

Memory Merge DQN: Sensitivity Weighted Target Updates for Stable Value Learning Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T08:51:16.467225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2385eab2-2099-4682-9064-1067413c5372 · inbound

Sustaining Plasticity via Learnable Wavelet Activations in Continual Learning cites this paper.

Sustaining Plasticity via Learnable Wavelet Activations in Continual Learning Understanding and Preventing Capacity Loss in Reinforcement Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T21:38:06.725532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.