Paper Citation Record · LEDGER

Unsupervised-to-Online Reinforcement Learning

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2408.14785.

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

pith.paper-citation-record.v1
2408.14785 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:26:09.287491Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T10:27:14.524276Z

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 9b2dc2a4-ad80-4a17-a20c-3ee239e61086 · inbound

Skill Expansion and Composition in Parameter Space cites this paper.

Skill Expansion and Composition in Parameter Space Unsupervised-to-Online Reinforcement Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T17:26:09.287491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e72e18b-3a0a-4622-8c42-40d5b7f2d5b6 · inbound

Intention-Conditioned Flow Occupancy Models cites this paper.

Intention-Conditioned Flow Occupancy Models Unsupervised-to-Online Reinforcement Learning

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-19T10:27:14.525971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2c251867-e517-4ee3-aba5-6d85d7104971 · inbound

Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding cites this paper.

Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding Unsupervised-to-Online Reinforcement Learning

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:45:56.266679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bcf2f5aa-987b-4500-96dc-c86984bc1330 · inbound

Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding cites this paper.

Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding Unsupervised-to-Online Reinforcement Learning

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:42:30.552942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.