Paper Citation Record · LEDGER

Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2403.05996.

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

pith.paper-citation-record.v1
2403.05996 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:48:47.043510Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T16:36:17.868966Z

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 9a1b4d42-adff-45e9-8b04-bdba794b3256 · inbound

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization cites this paper.

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T12:32:20.029295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69f86b3d-c90e-4df1-a115-c076f20e78ff · inbound

Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments cites this paper.

Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T12:10:02.108907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a66d4cc9-d173-42f1-ad54-72ae3ee518b3 · inbound

Scaling CrossQ with Weight Normalization cites this paper.

Scaling CrossQ with Weight Normalization Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T10:59:52.478488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f8e9553-8670-47dc-88c6-08aeae7753bd · inbound

What Does Flow Matching Bring To TD Learning? cites this paper.

What Does Flow Matching Bring To TD Learning? Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:36:17.872845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c3100326-b89c-44e6-b7ca-64a2b68fe80d · inbound

Distributional Value Estimation Without Target Networks for Robust Quality-Diversity cites this paper.

Distributional Value Estimation Without Target Networks for Robust Quality-Diversity Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:14:46.887537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2c51e85c-2116-48e5-9cf8-49073decd3b5 · inbound

RL Token: Bootstrapping Online RL with Vision-Language-Action Models cites this paper.

RL Token: Bootstrapping Online RL with Vision-Language-Action Models Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:26:09.169789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c0b067f1-f27f-40a4-b58b-018c3db489d3 · inbound

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control cites this paper.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

Reference 278

Resolution: unresolved; no resolver link, observed 2026-08-12T00:48:47.043510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.