Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1910.04281.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:52:31.735784Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T01:29:22.882961Z
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 d827501b-a0fe-48b6-b5b8-7de3a6b9ea3e · inbound
Improvement of Optimization using Learning Based Models in Mixed Integer Linear Programming Tasks cites this paper.
Improvement of Optimization using Learning Based Models in Mixed Integer Linear Programming Tasks Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:52:31.735784Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2fdfeb35-f613-4952-8c49-d2c8a44c354d · inbound
Do Less, Achieve More: Do We Need Every-Step Optimization for RL Fine-tuning of Diffusion Models? cites this paper.
Do Less, Achieve More: Do We Need Every-Step Optimization for RL Fine-tuning of Diffusion Models? Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:48:57.317233Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 51d8cc07-6db8-4ba3-981e-b530af6308a2 · inbound
TacCoRL: Integrating Tactile Feedback into VLA via Simulation cites this paper.
TacCoRL: Integrating Tactile Feedback into VLA via Simulation Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
Reference 56
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T10:48:02.226789Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c42ccdde-2c1a-4ef1-8a4d-99ec7f6deefa · inbound
DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning cites this paper.
DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T01:29:22.884235Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.