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Paper Citation Record · LEDGER

Acme: A Research Framework for Distributed Reinforcement Learning

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2006.00979.

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

pith.paper-citation-record.v1
2006.00979 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 2026-08-16T06:30:59.297886+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:47:05.678159Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T08:56:06.429861Z

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 ec04d807-1c90-41d3-9deb-dc9908270b07 · inbound

What Matters in Learning from Offline Human Demonstrations for Robot Manipulation cites this paper.

What Matters in Learning from Offline Human Demonstrations for Robot Manipulation Acme: A Research Framework for Distributed Reinforcement Learning

Reference 89

Resolution
malformed identifier
arxiv_id, observed 2026-05-13T08:51:56.040675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-13T08:51:55.826747Z digest=sha256:5baa7eede699c710bcbf44f97b124682aa42e8c84d1e7949d7fcb372ceea9cb3

Observation 5f29c5f8-5d56-4c18-9762-521033fbe68f · inbound

Mastering Diverse Domains through World Models cites this paper.

Mastering Diverse Domains through World Models Acme: A Research Framework for Distributed Reinforcement Learning

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:08:22.356343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-11T09:08:21.677362Z digest=sha256:c3e437524a03e731b5f7578f9e05b12299c5e8d93b81fc4a65e14b6f6441317c

Observation 77ccfb99-dc48-46df-a1fa-7ddf810d37b7 · inbound

Learning Interactive Real-World Simulators cites this paper.

Learning Interactive Real-World Simulators Acme: A Research Framework for Distributed Reinforcement Learning

Reference 147

Resolution
metadata mismatch
arxiv_id, observed 2026-05-16T02:15:18.422564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-16T02:15:18.265190Z digest=sha256:8cdfd6e48f382ef66753e37e902e67c67bdae34e449951c9fb12d8cd2c5ddc00

Observation 5a4769fb-9a4c-44e1-934a-12ae09581d4b · inbound

Gymnasium: A Standard Interface for Reinforcement Learning Environments cites this paper.

Gymnasium: A Standard Interface for Reinforcement Learning Environments Acme: A Research Framework for Distributed Reinforcement Learning

Reference 16

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T17:29:49.403651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-11T17:29:49.186565Z digest=sha256:8f3fbdddd9db3662310c204624682ab4d0fe42340f9441d48bd8ba2ee4c7bd22

Observation 10bce5cd-5b4a-43de-a27b-1ac77cf10cca · inbound

Revisiting Generative Policies: A Simpler Reinforcement Learning Algorithmic Perspective cites this paper.

Revisiting Generative Policies: A Simpler Reinforcement Learning Algorithmic Perspective Acme: A Research Framework for Distributed Reinforcement Learning

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-12T04:36:35.832000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T04:36:35.832000Z digest=sha256:612efbb8004fcaff04d0b299acb78d653b94db6f9e0ac65911983e4d6c4d7c74

Observation 7d11bd31-7012-43f3-a93b-5171b8304997 · inbound

PIMAEX: Multi-Agent Exploration through Peer Incentivization cites this paper.

PIMAEX: Multi-Agent Exploration through Peer Incentivization Acme: A Research Framework for Distributed Reinforcement Learning

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-10T22:35:13.612988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:13.612988Z digest=sha256:39c0889a142f10afd2c2ed04f965f0e0be4d6969c06dda753678ac6c7446f140

Observation 2fd903b9-9f74-4b6a-b4c6-bf191014cea0 · inbound

Wasserstein Policy Optimization cites this paper.

Wasserstein Policy Optimization Acme: A Research Framework for Distributed Reinforcement Learning

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-16T04:47:05.678159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:47:05.678159Z digest=sha256:55376e67b26a9e48dbdcac5b20b3fdd1d3e42ceb7ea732aaf539b0843dc95616

Observation b0bbf672-6487-46dd-a7cc-4f670b6cc976 · 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 Acme: A Research Framework for Distributed Reinforcement Learning

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-19T06:42:07.618611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-19T06:38:33.969927Z digest=sha256:f706924191e4a19b33fd9636d06c37529018eedd40bb0dcdeae45a247c511b84

Observation 3e505bb6-a99a-4cf9-9558-1c793b2d0777 · inbound

Reinforcement Learning for Machine Learning Engineering Agents cites this paper.

Reinforcement Learning for Machine Learning Engineering Agents Acme: A Research Framework for Distributed Reinforcement Learning

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-05T12:24:02.264694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:24:02.264694Z digest=sha256:1718518a1bef6bb83137cd46c259cb86048e88d46fdf1ebd9201a8469b6370e5

Observation 7fea58c4-5d21-41e7-b3ad-db14a5e1550c · inbound

Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces cites this paper.

Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces Acme: A Research Framework for Distributed Reinforcement Learning

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-21T21:15:38.685191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-21T21:14:54.053177Z digest=sha256:308e7d74b8591331f9fb272a55a6b8d33f11a9d850a59da6a2454a6abee9cc9c

Observation 3a1b20da-e3a2-4b93-8472-8b2f65e442fb · inbound

Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning cites this paper.

Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning Acme: A Research Framework for Distributed Reinforcement Learning

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-07-09T08:56:06.431374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-07-09T08:51:10.098370Z digest=sha256:615a83ccecca08b9af0d8d213feceedf7df0be50298d877ccf58a91c8875d038

Observation e96a2094-9329-499b-a890-ee6a5ac6b5fb · inbound

Koopman Dreamer: Spectrally Constrained Latent Dynamics for Stable World-Model Imagination cites this paper.

Koopman Dreamer: Spectrally Constrained Latent Dynamics for Stable World-Model Imagination Acme: A Research Framework for Distributed Reinforcement Learning

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-01T11:59:46.835402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T11:59:46.835402Z digest=sha256:76e8948c1e6f5b992353ee35b6ccd120558db1a3cd75aaea29074eae707fc6b5