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Paper Citation Record · LEDGER

Towards model-free RL algorithms that scale well with unstructured data

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2311.02215.

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

pith.paper-citation-record.v1
2311.02215 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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T11:09:53.114264Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T18:01:47.394239Z

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 00377648-d430-425a-9209-2b49a9a582b3 · inbound

On the Interplay Between Sparsity and Training in Deep Reinforcement Learning cites this paper.

On the Interplay Between Sparsity and Training in Deep Reinforcement Learning Towards model-free RL algorithms that scale well with unstructured data

Reference 2010

Resolution
unresolved
no resolver link, observed 2026-08-10T11:09:53.114264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T11:09:53.114264Z digest=sha256:9800e4c60b1b613fc03faa5c79df2fb30a7c95d7c14ecca08eb02ffa60144efd

Observation c9259be3-76b0-4b10-a6a0-c393a0c51e4d · inbound

An Analysis of Action-Value Temporal-Difference Methods That Learn State Values cites this paper.

An Analysis of Action-Value Temporal-Difference Methods That Learn State Values Towards model-free RL algorithms that scale well with unstructured data

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:01:47.519541Z

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=2026-08-06T18:01:45.917099Z digest=sha256:447fa99ecefca04f1ddf323e37b86f5df36834b3897cc42f5af2d280cb583e80