Paper Citation Record · LEDGER

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control

As of 16 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2608.07086.

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

pith.paper-citation-record.v1
2608.07086 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:31:05.142660Z

measured 34 of 34 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact2
verified fuzzy19
unresolved13
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7b645c31-9859-436c-b7ad-91e7c44f0365 · outbound

This paper cites Deep reinforcement learning at the edge of the statistical precipice.Advances in Neural Information Processing Systems, 2021.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Deep reinforcement learning at the edge of the statistical precipice.Advances in Neural Information Processing Systems, 2021

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.830406Z

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Observation a71db8cf-97de-4be7-8d0a-4350e0effaef · outbound

This paper cites PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T14:31:05.006890Z

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Observation 5cc07015-ee2e-4b2d-b84d-dbc3a1c4fab9 · outbound

This paper cites Representation Learning: A Review and New Perspectives.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Representation Learning: A Review and New Perspectives

Reference 3

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Observation f7c176e3-440d-42da-b1f1-e688d0d61fc8 · outbound

This paper cites Revisitingrainbow: Promotingmoreinsightful andinclusivedeepreinforcementlearningresearch.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Revisitingrainbow: Promotingmoreinsightful andinclusivedeepreinforcementlearningresearch

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.817149Z

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Observation 55b224c6-c5f6-490a-9dc5-48842f814911 · outbound

This paper cites Beyond the rainbow: High perfor- mance deep reinforcement learning on a desktop PC.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Beyond the rainbow: High perfor- mance deep reinforcement learning on a desktop PC

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.803930Z

Source-reported events for the cited work

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Observation a2d0ff16-9a26-45c0-82a9-88788c052046 · outbound

This paper cites Revisiting Fundamentals of Experience Replay.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Revisiting Fundamentals of Experience Replay

Reference 6

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Source-reported events for the cited work

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Observation 18f84dbe-49c6-41ac-a648-658a38c48b85 · outbound

This paper cites An equivalence between loss functions and non-uniform sampling in experience replay.Advances in Neural Information Processing Systems, 33,.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control An equivalence between loss functions and non-uniform sampling in experience replay.Advances in Neural Information Processing Systems, 33,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.791033Z

Source-reported events for the cited work

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Observation ba8afdf8-a6dd-497e-b95a-3865bba1c3a7 · outbound

This paper cites Smith, Shixiang Shane Gu, Doina Precup, and David Meger.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Smith, Shixiang Shane Gu, Doina Precup, and David Meger

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.778291Z

Source-reported events for the cited work

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Observation 114a4593-5437-4a6a-b95c-95c297cbd53c · outbound

This paper cites Towards general- purpose model-free reinforcement learning.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Towards general- purpose model-free reinforcement learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.764984Z

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.

Observation 198838a9-a1ee-4b99-b3f1-36f25938948d · outbound

This paper cites an unresolved cited work.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-15T14:31:05.751431Z

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.

Observation b0424f19-834a-4528-ad26-79e415c6cc07 · outbound

This paper cites World models.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control World models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T14:31:05.046493Z

Source-reported events for the cited work

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Observation 6f7029ca-cfb7-4d49-8b67-7b6183bc9427 · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.738365Z

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.

Observation b9b0687c-193a-4f11-acb9-2980ee440fc7 · outbound

This paper cites Dream to control: Learning behaviors by latent imagination.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Dream to control: Learning behaviors by latent imagination

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.725965Z

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.

Observation 4a5a9464-f724-4d99-b55e-565db405084a · outbound

This paper cites Td-mpc2: Scalable,robustworldmodelsforcontinuous control.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Td-mpc2: Scalable,robustworldmodelsforcontinuous control

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.712833Z

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.

Observation 75521aa7-6adf-44cc-a9fc-4f6f63778aea · outbound

This paper cites Rainbow: Combining improvements in deep reinforcement learning.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Rainbow: Combining improvements in deep reinforcement learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.699017Z

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.

Observation 5edb7225-8d95-4cf5-93a0-1e30b94bf0a9 · outbound

This paper cites Plasticity Loss in Deep Reinforcement Learning: A Survey.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Plasticity Loss in Deep Reinforcement Learning: A Survey

Reference 16

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Source-reported events for the cited work

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Observation 9dabe8db-d422-4f46-a02e-93d1151f35b2 · outbound

This paper cites Mastering massive multi-task reinforcement learning via mixture-of-expert decision transformer.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Mastering massive multi-task reinforcement learning via mixture-of-expert decision transformer

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.686469Z

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.

Observation 53a4a274-5cb8-4031-b1bf-7753a63c9128 · outbound

This paper cites QPO: Query-dependent prompt optimization via multi-loop offline reinforcement learning.Transactions on Machine Learning Research, 2025.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control QPO: Query-dependent prompt optimization via multi-loop offline reinforcement learning.Transactions on Machine Learning Research, 2025

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.660678Z

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.

Observation 319fb73b-2c66-47d1-b57c-ff3b3b2d30a4 · outbound

This paper cites Large batch experience replay, 2021.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Large batch experience replay, 2021

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.647586Z

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.

Observation cdcacc60-feda-444a-834f-e39b00bab801 · outbound

This paper cites Wurman, Jaegul Choo, Peter Stone, and Takuma Seno.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Wurman, Jaegul Choo, Peter Stone, and Takuma Seno

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.634494Z

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.

Observation 1c377562-335d-4156-a0eb-9dc78448d871 · outbound

This paper cites Hyperspherical normalization for scalable deep reinforcement learning.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Hyperspherical normalization for scalable deep reinforcement learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.620487Z

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.

Observation 4b4e2654-1194-4ec2-9809-600e16731e56 · outbound

This paper cites Rethinking the role of dynamic sparse training for scalable deep reinforcement learning, 2025.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Rethinking the role of dynamic sparse training for scalable deep reinforcement learning, 2025

Reference 22

Resolution: verified exact; raw_fallback, observed 2026-08-15T14:31:05.388872Z

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.

Observation 0b3d14d8-c749-46db-8cfd-92fab8f3f293 · outbound

This paper cites Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, and Pulkit Agrawal.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, and Pulkit Agrawal

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.607191Z

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.

Observation 214917d2-f8fb-4d65-8fea-3b5a87a08529 · outbound

This paper cites Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 24

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Source-reported events for the cited work

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Observation 2569f3ca-d3d4-4a04-a81e-6035a01de763 · outbound

This paper cites The primacy bias in deep reinforcement learning.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control The primacy bias in deep reinforcement learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.593965Z

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.

Observation 69a707fb-f985-4874-927a-b6bb18f8cca8 · outbound

This paper cites Prioritized Experience Replay.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Prioritized Experience Replay

Reference 26

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Source-reported events for the cited work

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Observation 8baa9996-7acb-4b10-893b-254deb7763dd · outbound

This paper cites HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T14:31:05.113954Z

Source-reported events for the cited work

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Observation 06849875-f7d8-46b1-a0aa-5e34fec23a11 · outbound

This paper cites CURL: Contrastive Unsupervised Representations for Reinforcement Learning.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control CURL: Contrastive Unsupervised Representations for Reinforcement Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T14:31:05.118443Z

Source-reported events for the cited work

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Observation 06d296aa-09b2-4fc3-aa9d-c15e9bbee99f · outbound

This paper cites Prioritizing Samples in Reinforcement Learning with Reducible Loss.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Prioritizing Samples in Reinforcement Learning with Reducible Loss

Reference 29

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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.

Observation 1f82b7ca-3752-4d2f-b1cd-af5539c2c183 · outbound

This paper cites MaxinfoRL: Boosting exploration in reinforcement learning through information gain maximization.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control MaxinfoRL: Boosting exploration in reinforcement learning through information gain maximization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.580941Z

Source-reported events for the cited work

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Observation 9976e6e9-1ab3-4df9-a4b3-ea6db16b9e2e · outbound

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Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control DeepMind Control Suite

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T14:31:05.133424Z

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Observation 5fe39764-39e3-424f-b0ed-7278b0939307 · outbound

This paper cites MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T14:31:05.138058Z

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Unavailable: canonical work link unavailable.

Observation 4c5b25b3-c1e9-4c35-9c75-c2b123dd9bd7 · outbound

This paper cites Towards sample efficient reinforcement learning.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Towards sample efficient reinforcement learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:31:05.568934Z

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.

Observation b2c3eb7a-f0ca-48c1-a217-f1132a55ecc6 · outbound

This paper cites an unresolved cited work.

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control Unresolved cited work

Reference 2025

Resolution: unresolved; raw_fallback, observed 2026-08-15T14:31:05.673659Z

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

Pith citing papers

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