Paper Citation Record · LEDGER

Natural Language Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2411.14251.

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

pith.paper-citation-record.v1
2411.14251 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:15:15.885559Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:16:44.965693Z

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 1d0bc4b8-304c-47b1-b80c-88712380399e · inbound

elsciRL: Integrating Language Solutions into Reinforcement Learning Problem Settings cites this paper.

elsciRL: Integrating Language Solutions into Reinforcement Learning Problem Settings Natural Language Reinforcement Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T18:17:51.412053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c032603-e570-4a06-8d29-73791bff4a0d · inbound

LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra cites this paper.

LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra Natural Language Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T15:28:42.368027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a793711b-2543-4159-9e11-a314f0d7daa6 · inbound

Learning from Language Feedback via Variational Policy Distillation cites this paper.

Learning from Language Feedback via Variational Policy Distillation Natural Language Reinforcement Learning

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:39:00.368600Z

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.

Observation 62af02a2-3793-4ca9-9930-1bf60acf34a8 · inbound

When Clients Stop Following: A Cognitive Conceptualization Diagram-driven Framework for Strategic Counseling cites this paper.

When Clients Stop Following: A Cognitive Conceptualization Diagram-driven Framework for Strategic Counseling Natural Language Reinforcement Learning

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T07:16:44.967417Z

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.

Observation 0a90098d-cd39-4f92-a217-89194b133d30 · inbound

Instruction-Conditioned Exploration for Reinforcement Learning with Self-Distillation to an Unconditioned Policy cites this paper.

Instruction-Conditioned Exploration for Reinforcement Learning with Self-Distillation to an Unconditioned Policy Natural Language Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T15:25:49.260514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9be8e652-2fb8-4dc0-ad9b-9062167e5bbf · inbound

Instruction-Conditioned Exploration for Reinforcement Learning with Self-Distillation to an Unconditioned Policy cites this paper.

Instruction-Conditioned Exploration for Reinforcement Learning with Self-Distillation to an Unconditioned Policy Natural Language Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T00:15:15.885559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.