Paper Citation Record · LEDGER

DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2310.19668.

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

pith.paper-citation-record.v1
2310.19668 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:32:53.901414Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T22:21:49.407343Z

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 0f10747d-6b07-4af0-b75b-552092ec3032 · inbound

The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning cites this paper.

The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T00:32:53.901414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b6e9519-2ae4-4ab0-9ec0-1e12b356ec73 · inbound

Residual Reward Models for Preference-based Reinforcement Learning cites this paper.

Residual Reward Models for Preference-based Reinforcement Learning DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T21:17:42.181860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3de77387-dce0-45db-97f7-38563c7bb890 · inbound

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control cites this paper.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.879014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8b3b226-75c5-494c-afe0-743973e53027 · inbound

Agent-Centric Observation Adaptation for Robust Visual Control under Dynamic Perturbations cites this paper.

Agent-Centric Observation Adaptation for Robust Visual Control under Dynamic Perturbations DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:21:49.484256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f6fc8ad4-a7f3-4c12-bc63-ee0b631429fb · inbound

Agent-Centric Observation Adaptation for Robust Visual Control under Dynamic Perturbations cites this paper.

Agent-Centric Observation Adaptation for Robust Visual Control under Dynamic Perturbations DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:50:49.824477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dade4dfc-3cb6-401e-85af-588d4fab6a1f · inbound

Reinforcement Learning for the Full Strawberry Harvesting Process: Obstacle Separation, Detachment, and Placement cites this paper.

Reinforcement Learning for the Full Strawberry Harvesting Process: Obstacle Separation, Detachment, and Placement DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T01:23:37.685746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.