Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2403.09583.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T16:37:34.676237Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T02:26:26.974524Z
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 624e80c9-34b7-4612-9041-2ea421a3ff3b · inbound
An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems cites this paper.
An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
Reference 135
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:51:52.682536Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 41423878-e643-4d71-a570-f1945e5c1435 · inbound
Training-free Generation of Temporally Consistent Rewards from VLMs cites this paper.
Training-free Generation of Temporally Consistent Rewards from VLMs ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:47:41.236915Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b1423f83-2ad9-4fa0-ba94-19930ec9985a · inbound
Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Models cites this paper.
Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Models ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
Reference 140
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:37:34.676237Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e599a50b-cceb-4295-ae3c-d470fff19073 · inbound
Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing cites this paper.
Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
Reference 106
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T20:49:37.963470Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 83fa3052-c748-4340-a909-f342273640ea · inbound
Curriculum-Based Multi-Tier Semantic Exploration via Deep Reinforcement Learning cites this paper.
Curriculum-Based Multi-Tier Semantic Exploration via Deep Reinforcement Learning ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T19:17:00.606863Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 53881d46-9304-4121-87e9-313486866ddb · inbound
Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning cites this paper.
Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
Reference 80
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T23:15:48.960469Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f7b47828-442e-4f1b-acea-ba885c1410aa · inbound
Reinforcement Learning from Cross-domain Videos with Video Prediction Model cites this paper.
Reinforcement Learning from Cross-domain Videos with Video Prediction Model ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
Reference 24
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T02:26:26.976160Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.