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Paper Citation Record · LEDGER

Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 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 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:10:02.108907Z

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 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.

source=arxiv_source observed=2026-08-07T12:10:02.108907Z digest=sha256:3b86d24f54d365597bc9551079565965977c8ed9b93ce74f861e5bff611e0cd3

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.

source=arxiv_source observed=2026-08-07T10:59:52.478488Z digest=sha256:ce9eb403543b419d3a00dfa9c5ea858e94979954fd268e801217be1f85bada91

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-15T16:32:29.432272Z digest=sha256:1168988cc50d91173b15ad16c9738a22784166695037441bcfb921466cde4b1a

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T00:11:04.222842Z digest=sha256:fa96a189e8d89b1380cb9817f5cfd9b47efd89b599b2f4b5327b143878495794

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-08T11:56:34.978806Z digest=sha256:38bbbdd4d87b2b335613da926e1306bc57578f9e3caacf95ec1318561c8687e8