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Paper Citation Record · LEDGER

Neural PPO-Clip Attains Global Optimality: A Hinge Loss Perspective

As of 3 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2110.13799.

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

pith.paper-citation-record.v1
2110.13799 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 2026-08-03T06:30:56.289259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-25T21:17:28.832301Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:30:07.606033Z

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 5500c30a-bf41-446d-8bfe-a68afde016f7 · inbound

Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation cites this paper.

Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation Neural PPO-Clip Attains Global Optimality: A Hinge Loss Perspective

Reference 135

Resolution
verified exact
arxiv_id, observed 2026-05-20T12:48:17.561580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=arxiv_source observed=2026-05-20T12:44:29.147095Z digest=sha256:b41341f8868ba001347cd5a3417dc4fa2ffe471ff27b6c61f108e3963930defa

Observation fb52bd12-3557-4508-8120-10976269fba7 · inbound

Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning cites this paper.

Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning Neural PPO-Clip Attains Global Optimality: A Hinge Loss Perspective

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-07-04T19:30:07.607563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

source=pdf_text observed=2026-06-25T21:17:28.832301Z digest=sha256:b788e2e20fa91dff7e25f9849d8856d005d75fad4da16389d5303a85bfb11a62