Paper Citation Record · LEDGER

Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2010.14498.

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

pith.paper-citation-record.v1
2010.14498 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:43:34.479997Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T21:08:57.724327Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 1b457865-6a91-4366-8127-2f5e715ecb2d · inbound

One Step Diffusion via Shortcut Models cites this paper.

One Step Diffusion via Shortcut Models Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-15T06:40:55.497783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2b7e8b45-47b3-4ba0-9f99-2c2a4f6f78fa · 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 Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T15:43:34.479997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b363a0a-09f7-4c51-aa25-2cee7c1c03fb · inbound

Recovering Plasticity of Neural Networks via Soft Weight Rescaling cites this paper.

Recovering Plasticity of Neural Networks via Soft Weight Rescaling Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:23.029630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e27e190-44ef-418b-ae15-d7c59f067f8a · inbound

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

Activation Function Design Sustains Plasticity in Continual Learning Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:01:23.646880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f6da132d-5a88-46fa-bbe5-096ee3c8db78 · inbound

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

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:05:08.919841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a1899781-194d-4dcc-afe7-34ced7fd8cc3 · inbound

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

Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

Reference 14

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 1718f413-fe4c-44bf-b62e-73034d7fa408 · inbound

Don't Let Gains FADE: Breaking Down Policy Gradient Weights in RL cites this paper.

Don't Let Gains FADE: Breaking Down Policy Gradient Weights in RL Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:08:57.726023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 67b27c5e-a6e0-488e-9efd-9d56784b133d · inbound

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning cites this paper.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:05.867960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.