Paper Citation Record · LEDGER

Offline Actor-Critic Reinforcement Learning Scales to Large Models

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.05546.

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

pith.paper-citation-record.v1
2402.05546 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 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:52:13.872247Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T05:09:45.465982Z

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 f3783051-a2a2-4173-8796-850d8c7f8e47 · inbound

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers cites this paper.

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers Offline Actor-Critic Reinforcement Learning Scales to Large Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T18:52:13.872247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fd1525d-603f-4a53-ac93-b0bff6b1cd72 · inbound

Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning cites this paper.

Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning Offline Actor-Critic Reinforcement Learning Scales to Large Models

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:00.682182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2a9d3851-5ace-40a1-9200-323577193394 · inbound

Sample-Mean Anchored Thompson Sampling for Offline-to-Online Learning with Distribution Shift cites this paper.

Sample-Mean Anchored Thompson Sampling for Offline-to-Online Learning with Distribution Shift Offline Actor-Critic Reinforcement Learning Scales to Large Models

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:01:25.007746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ddcce864-c00e-498b-8a70-f9ae7fdded99 · inbound

Sample-Mean Anchored Thompson Sampling for Offline-to-Online Learning with Distribution Shift cites this paper.

Sample-Mean Anchored Thompson Sampling for Offline-to-Online Learning with Distribution Shift Offline Actor-Critic Reinforcement Learning Scales to Large Models

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:09:45.471632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.