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Paper Citation Record · LEDGER

Measuring the Reliability of Reinforcement Learning Algorithms

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1912.05663.

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

pith.paper-citation-record.v1
1912.05663 v2

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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:15:09.845886Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T08:43:15.331093Z

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 339e7f89-55ec-4de5-a959-0e9b60b36113 · inbound

SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity cites this paper.

SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity Measuring the Reliability of Reinforcement Learning Algorithms

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-03T03:15:09.845886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:15:09.845886Z digest=sha256:9450de17e7944eac0b630fd6230f20ab6440c1bdf6068b93dc6a80119810d339

Observation 018240d6-bb79-4b53-a23e-a436f655a5a6 · inbound

On Effectiveness and Efficiency of Agentic Tool-calling and RL Training cites this paper.

On Effectiveness and Efficiency of Agentic Tool-calling and RL Training Measuring the Reliability of Reinforcement Learning Algorithms

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-06-29T08:43:15.332447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-29T08:38:52.411671Z digest=sha256:1f3b2a7b0366139a4ecb075375773a7ea2bed741afe81e36c0ce7101b2026463