Paper Citation Record · LEDGER

The Benefits of Model-Based Generalization in Reinforcement Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2211.02222.

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

pith.paper-citation-record.v1
2211.02222 v3

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 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T21:04:13.166324Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T23:21:26.082096Z

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 110e2edc-ef02-455c-b69d-cfd2c7b35601 · inbound

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions cites this paper.

Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions The Benefits of Model-Based Generalization in Reinforcement Learning

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:40:41.279962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4930d6a2-e324-4fa0-a08d-eca3e146ee5d · inbound

Learning to Theorize the World from Observation cites this paper.

Learning to Theorize the World from Observation The Benefits of Model-Based Generalization in Reinforcement Learning

Reference 288

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:21:26.089767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d59d1aa4-e79c-459f-8521-3356045c9050 · inbound

When to Plan: Learning to Select Between Reactive Control and Deliberative Planning cites this paper.

When to Plan: Learning to Select Between Reactive Control and Deliberative Planning The Benefits of Model-Based Generalization in Reinforcement Learning

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-01T21:04:13.166324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f093cc6-6427-4f78-ab61-69444a4982ce · inbound

World Action Planner: Generalizable Decision-Making with Action-Conditioned World Models cites this paper.

World Action Planner: Generalizable Decision-Making with Action-Conditioned World Models The Benefits of Model-Based Generalization in Reinforcement Learning

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-01T04:59:39.399377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.