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Paper Citation Record · LEDGER

Beyond the Boundaries of Proximal Policy Optimization

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.00666.

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

pith.paper-citation-record.v1
2411.00666 v1

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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T18:45:23.170247Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T11:35:19.044780Z

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 058f5053-c8a0-4149-a692-54718f1bd4a8 · inbound

Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments cites this paper.

Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments Beyond the Boundaries of Proximal Policy Optimization

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-02T18:45:23.170247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T18:45:23.170247Z digest=sha256:e676a876258fc8730ffb897b72e735d45cbc0df18bb9338f83fb64a97ebd01e5

Observation 2f0d50e0-d134-4ab1-8ad7-78cc8fffc9f6 · inbound

Bounded Ratio Reinforcement Learning cites this paper.

Bounded Ratio Reinforcement Learning Beyond the Boundaries of Proximal Policy Optimization

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-10T11:35:19.046157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-10T04:50:11.020901Z digest=sha256:bf47326de3e379812ee5d339b8d7cc4e4b6c225618e720ebf56704e719f30212