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Paper Citation Record · LEDGER

Guiding Pretraining in Reinforcement Learning with Large Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2302.06692.

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

pith.paper-citation-record.v1
2302.06692 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:48:17.829460Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

39
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9e400080-fe70-4a51-ae39-f0ec403781d9 · inbound

VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models cites this paper.

VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models Guiding Pretraining in Reinforcement Learning with Large Language Models

Reference 80

Resolution
verified exact
arxiv_id, observed 2026-05-13T08:57:22.340218Z

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.

source=pdf_text observed=2026-05-13T08:57:22.299028Z digest=sha256:1a6b5adeaf8918f9147c574afb5454ca06ced3653838be4e45373d0aef94bddc

Observation adf21c04-f06f-4385-af9c-9088f5632006 · inbound

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution cites this paper.

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Guiding Pretraining in Reinforcement Learning with Large Language Models

Reference 96

Resolution
verified exact
arxiv_id, observed 2026-05-16T08:12:31.448332Z

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.

source=arxiv_source observed=2026-05-16T08:12:30.984870Z digest=sha256:0c5465f9fad6b1a1bc1a86bb4847975013710c489fcddfd23d78325134b528c0

Observation 4feb75f3-11bc-49c7-a1f2-d01abc513c3e · inbound

Learning Interactive Real-World Simulators cites this paper.

Learning Interactive Real-World Simulators Guiding Pretraining in Reinforcement Learning with Large Language Models

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-16T02:15:18.575048Z

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.

source=arxiv_source observed=2026-05-16T02:15:18.265190Z digest=sha256:2b8db98c25f15fcb1fd73a83f3472791a08897b9c40ee27a0dec99c30cf12946

Observation 69549246-4d4f-4c4b-9981-7ba6beb04456 · inbound

Application of LLMs to Multi-Robot Path Planning and Task Allocation cites this paper.

Application of LLMs to Multi-Robot Path Planning and Task Allocation Guiding Pretraining in Reinforcement Learning with Large Language Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:17.829460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:17.829460Z digest=sha256:ee0c28f28b7f78ab2040fbbdf20edc5c085e7f6893da795f021d8904848e85b1

Observation f0f210ad-47a6-452d-a68c-cb1f558b71f4 · inbound

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills cites this paper.

Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills Guiding Pretraining in Reinforcement Learning with Large Language Models

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-04T19:45:29.172857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T19:45:29.172857Z digest=sha256:83a2ddb702a8211961935dca5e286abac3efbe3222662f5cd54a6656aceab111