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Paper Citation Record · LEDGER

Efficient Wasserstein Natural Gradients for Reinforcement Learning

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2010.05380.

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

pith.paper-citation-record.v1
2010.05380 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:47:05.722747Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T22:24:00.335031Z

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 e144be77-e467-494d-945b-4a65c3f7f94a · inbound

Wasserstein Policy Optimization cites this paper.

Wasserstein Policy Optimization Efficient Wasserstein Natural Gradients for Reinforcement Learning

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-16T04:47:05.722747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:47:05.722747Z digest=sha256:caf1779839ec353cc51a1103bf0aae5f4e25ef7831396a48c0668d4a056a14ab

Observation eb23ec28-a212-4cd1-8490-d96baf2e4beb · inbound

Global Convergence of Wasserstein Policy Gradient for Entropy-Regularized Reinforcement Learning cites this paper.

Global Convergence of Wasserstein Policy Gradient for Entropy-Regularized Reinforcement Learning Efficient Wasserstein Natural Gradients for Reinforcement Learning

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-06-29T22:24:00.336798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-29T22:19:53.934535Z digest=sha256:a8c927cb4c9a3f37b5bdff52ea3086d4ef541e943038e7ec675c85a9ecad7f2f