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Paper Citation Record · LEDGER

Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.19009.

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

pith.paper-citation-record.v1
2502.19009 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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:48:51.431138Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T23:39:05.052189Z

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 d765ea3d-991b-4127-946d-725e108653ad · inbound

How Should We Meta-Learn Reinforcement Learning Algorithms? cites this paper.

How Should We Meta-Learn Reinforcement Learning Algorithms? Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-06T14:48:51.431138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:48:51.431138Z digest=sha256:0ef04144516564097b082a027275135e28da7bd722a9b01c718e51853df6bc11

Observation 90f4f37f-7a34-4639-901c-12e49623065e · inbound

Reinforcement Learning Foundation Models Should Already Be A Thing cites this paper.

Reinforcement Learning Foundation Models Should Already Be A Thing Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-07-03T23:39:05.054742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-26T21:50:00.974590Z digest=sha256:5af753fcfcd3ab66af2edd9d50e8dd0164e2c9e2125232417ebde47cb24180fa