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Paper Citation Record · LEDGER

Distillation Strategies for Proximal Policy Optimization

As of 3 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1901.08128.

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

pith.paper-citation-record.v1
1901.08128 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T20:36:59.298711Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-23T23:13:36.943067Z

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 367b86ea-179c-4e57-8668-5856b5f93293 · inbound

Proximal Policy Distillation cites this paper.

Proximal Policy Distillation Distillation Strategies for Proximal Policy Optimization

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-05-23T23:13:36.945972Z

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=2026-05-23T23:09:42.421333Z digest=sha256:5ecde10a7b4936d1b19c52e0b3244111cab9b48bde2cd207cbf6815d9df9e5c2

Observation 04ec6e38-e7c4-4f81-b137-82a512469c95 · inbound

Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents cites this paper.

Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents Distillation Strategies for Proximal Policy Optimization

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-10T13:05:25.039259Z

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=2026-05-10T13:02:45.029951Z digest=sha256:fd1f13d21a6be039a279df474bb573e8276aed8eb0bbd1aed2801686e1575003

Observation 8603ced8-ea75-4ea3-9edb-6acb4bc5fdcd · inbound

Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents cites this paper.

Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents Distillation Strategies for Proximal Policy Optimization

Reference 10

Resolution
unresolved
no resolver link, observed 2026-07-12T20:36:59.298711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T20:36:59.298711Z digest=sha256:a4192c34d89dbddb253c68d6b2ea73f8c628f206f73fb70a94f2e6606114bd0b