Paper Citation Record · LEDGER

Leveraging Procedural Generation to Benchmark Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:1912.01588.

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

pith.paper-citation-record.v1
1912.01588 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:59:40.090855Z

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

171
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 253eddd0-5bf8-4c84-9f40-fa56f9e8ab76 · inbound

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

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Leveraging Procedural Generation to Benchmark Reinforcement Learning

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:12:31.413770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 104aae9c-0c71-449a-9de8-c9c97c00416d · inbound

Proximal Policy Distillation cites this paper.

Proximal Policy Distillation Leveraging Procedural Generation to Benchmark Reinforcement Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:13:36.980164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b42d011d-d382-4b29-ae7a-c14f333f4633 · inbound

An Open-Source Software Toolkit & Benchmark Suite for the Evaluation and Adaptation of Multimodal Action Models cites this paper.

An Open-Source Software Toolkit & Benchmark Suite for the Evaluation and Adaptation of Multimodal Action Models Leveraging Procedural Generation to Benchmark Reinforcement Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T04:59:40.090855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d7ab01c-84ce-482a-990a-51ff1f76c9ce · inbound

Dyn-O: Building Structured World Models with Object-Centric Representations cites this paper.

Dyn-O: Building Structured World Models with Object-Centric Representations Leveraging Procedural Generation to Benchmark Reinforcement Learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T20:19:02.762041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d28784e-5ce8-4fe5-8a07-7470b597d00c · inbound

Preemptive Solving of Future Problems: Multitask Preplay in Humans and Machines cites this paper.

Preemptive Solving of Future Problems: Multitask Preplay in Humans and Machines Leveraging Procedural Generation to Benchmark Reinforcement Learning

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-19T06:42:07.614826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation be0497ab-3a38-4fa9-a49f-2eeb5dbd12c0 · inbound

Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics cites this paper.

Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics Leveraging Procedural Generation to Benchmark Reinforcement Learning

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T12:38:46.903783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1284f15-6fcf-4e2e-89e0-e9ca4c5edb42 · inbound

Benchmarking Partial Observability in Reinforcement Learning with a Suite of Memory-Improvable Domains cites this paper.

Benchmarking Partial Observability in Reinforcement Learning with a Suite of Memory-Improvable Domains Leveraging Procedural Generation to Benchmark Reinforcement Learning

Reference 1992

Resolution: unresolved; no resolver link, observed 2026-08-06T10:34:35.473241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41bf3f85-9e3b-4c37-bee0-20063b01e320 · inbound

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning cites this paper.

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning Leveraging Procedural Generation to Benchmark Reinforcement Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T05:21:35.522064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff30be29-828b-4205-8356-f5a39941ec16 · inbound

Understanding Goal Generalisation in Sequential Reinforcement Learning cites this paper.

Understanding Goal Generalisation in Sequential Reinforcement Learning Leveraging Procedural Generation to Benchmark Reinforcement Learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-25T04:50:20.671717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 70afcc4e-51ae-4410-9c78-348c1d0cdaef · inbound

CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization cites this paper.

CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization Leveraging Procedural Generation to Benchmark Reinforcement Learning

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:36:29.552457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d063ccc5-8c68-409d-b95c-a9aaa4d03dc4 · inbound

Reinforcement Learning Foundation Models Should Already Be A Thing cites this paper.

Reinforcement Learning Foundation Models Should Already Be A Thing Leveraging Procedural Generation to Benchmark Reinforcement Learning

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T23:39:05.044993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d81c660d-93d4-4273-92f3-c62ff223fc21 · inbound

Reasoning Core: Designing Broad Procedural Data for Completion-Supervised Reasoning Training cites this paper.

Reasoning Core: Designing Broad Procedural Data for Completion-Supervised Reasoning Training Leveraging Procedural Generation to Benchmark Reinforcement Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T04:19:48.168776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.