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Paper Citation Record · LEDGER

Coupled Local and Global World Models for Efficient First Order RL

As of 23 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2602.06219.

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

pith.paper-citation-record.v1
2602.06219 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T04:03:57.872787Z

measured 39 of 39 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

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measured 0 of 1 external citation measurements

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Reference resolution

39 of 39 outbound references displayed

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Outbound references

Observation cebd7c78-9047-44b0-9a55-8ac96f97ff2d · outbound

This paper cites Diffusion for world modeling: Visual details matter in atari.Advances in Neural Information Processing Sys- tems, 37, 2024.

Coupled Local and Global World Models for Efficient First Order RL Diffusion for world modeling: Visual details matter in atari.Advances in Neural Information Processing Sys- tems, 37, 2024

Reference 1

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Observation 608d48c3-97b4-4f94-8ae1-54d81e4ced4d · outbound

This paper cites First order model-based rl through decoupled backpropagation.

Coupled Local and Global World Models for Efficient First Order RL First order model-based rl through decoupled backpropagation

Reference 2

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Observation 13d19bea-9c3f-43f0-b51c-5409a67823db · outbound

This paper cites Sample-efficient reinforce- ment learning with stochastic ensemble value expansion.

Coupled Local and Global World Models for Efficient First Order RL Sample-efficient reinforce- ment learning with stochastic ensemble value expansion

Reference 3

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Observation 717707db-0e74-4b12-a98f-c20e760d5999 · outbound

This paper cites Soloparkour: Constrained reinforcement learning for visual locomotion from privileged experience.

Coupled Local and Global World Models for Efficient First Order RL Soloparkour: Constrained reinforcement learning for visual locomotion from privileged experience

Reference 4

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Observation fd44f585-4173-4798-89dd-648e29713ef3 · outbound

This paper cites Cat: Constraints as terminations for legged locomotion reinforcement learning.

Coupled Local and Global World Models for Efficient First Order RL Cat: Constraints as terminations for legged locomotion reinforcement learning

Reference 5

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source=pdf_text observed=2026-08-03T04:03:55.759638Z digest=sha256:9b064c27d1d52371336433805b69659283309edb7bbe0aa72c5d340867552d44

Observation 91e11f48-61b2-4a08-b27a-11f79d867a3b · outbound

This paper cites Diffu- sion forcing: Next-token prediction meets full-sequence diffusion.Advances in Neural Information Processing Systems, 37, 2024.

Coupled Local and Global World Models for Efficient First Order RL Diffu- sion forcing: Next-token prediction meets full-sequence diffusion.Advances in Neural Information Processing Systems, 37, 2024

Reference 6

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Observation 1cd695da-a2e2-4c94-bcd5-8f8e359f0244 · outbound

This paper cites Extreme parkour with legged robots.

Coupled Local and Global World Models for Efficient First Order RL Extreme parkour with legged robots

Reference 7

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Observation 4c4436d8-b8f3-4ee6-9ea7-e28d8d8a0941 · outbound

This paper cites Deep reinforcement learning in a handful of trials using probabilistic dynamics models.

Coupled Local and Global World Models for Efficient First Order RL Deep reinforcement learning in a handful of trials using probabilistic dynamics models

Reference 8

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source=pdf_text observed=2026-08-03T04:03:56.022004Z digest=sha256:45ca50386eba7b9d848921549eebe0b654e61b916ea77c2cea6414fe89167fac

Observation 274f3b92-1545-4ad3-be73-a0b1fd2ec2c9 · outbound

This paper cites Model- augmented actor-critic: Backpropagating through paths.

Coupled Local and Global World Models for Efficient First Order RL Model- augmented actor-critic: Backpropagating through paths

Reference 9

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Observation 2efb367e-e2e2-4b4c-98d6-c0745027ebd8 · outbound

This paper cites One Step Diffusion via Shortcut Models.

Coupled Local and Global World Models for Efficient First Order RL One Step Diffusion via Shortcut Models

Reference 10

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source=pdf_text observed=2026-08-03T04:03:56.241350Z digest=sha256:8050056bc9cb96c1deeeb152ae49358c718513d820020788ac4ee8cda3ef0e9f

Observation c724f07f-d680-4eaa-8913-584157685fc4 · outbound

This paper cites PWM: Policy Learning with Multi-Task World Models.

Coupled Local and Global World Models for Efficient First Order RL PWM: Policy Learning with Multi-Task World Models

Reference 11

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Observation 3bdc782b-677e-4961-995e-aa2203481aee · outbound

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

Coupled Local and Global World Models for Efficient First Order RL Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 12

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Observation f273cae3-e53d-4d1c-8099-b10c5b7bf316 · outbound

This paper cites Soft Actor-Critic Algorithms and Applications.

Coupled Local and Global World Models for Efficient First Order RL Soft Actor-Critic Algorithms and Applications

Reference 13

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source=pdf_text observed=2026-08-03T04:03:56.421007Z digest=sha256:c5afe5c9ebf94980dedb39bf1439b613a69915b9b0a53a292d2a5fccb996a2ee

Observation 8ed3ffc5-301e-433f-ad70-63d8859bbd60 · outbound

This paper cites Mastering atari with discrete world models.

Coupled Local and Global World Models for Efficient First Order RL Mastering atari with discrete world models

Reference 14

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source=pdf_text observed=2026-08-03T04:03:56.512264Z digest=sha256:90510dec9d72103ef35487388b4b18337cd3c6850a1f3f8eee0689d30c90f574

Observation 1797ab77-6b56-453b-9833-8cf4aadf30b5 · outbound

This paper cites Mastering diverse control tasks through world models.Nature, 640(8059):647–653, 2025.

Coupled Local and Global World Models for Efficient First Order RL Mastering diverse control tasks through world models.Nature, 640(8059):647–653, 2025

Reference 15

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Observation 1dae7f2e-5e52-447f-ad6b-0dc8b684048c · outbound

This paper cites Training Agents Inside of Scalable World Models.

Coupled Local and Global World Models for Efficient First Order RL Training Agents Inside of Scalable World Models

Reference 17

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Observation 8bd46494-f7ad-4d65-843d-ebdc11dad254 · outbound

This paper cites Temporal difference learning for model predictive control.

Coupled Local and Global World Models for Efficient First Order RL Temporal difference learning for model predictive control

Reference 18

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source=pdf_text observed=2026-08-03T04:03:56.764465Z digest=sha256:d7a6e6c4d780d0201dd2e97aec1afae1c348107042b66e48281470af0abea868

Observation b294454d-15bb-4b10-908a-2439cc741e2a · outbound

This paper cites Td-mpc2: Scalable, robust world models for continuous control.

Coupled Local and Global World Models for Efficient First Order RL Td-mpc2: Scalable, robust world models for continuous control

Reference 19

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source=pdf_text observed=2026-08-03T04:03:56.815535Z digest=sha256:c2bf7bbb9b7497b7cb0b6549179b929cd936109d493ee44c9798232d921d629c

Observation f2d99499-4fa2-432b-abe9-c8419db54712 · outbound

This paper cites Axial attention in multidimensional transformers.

Coupled Local and Global World Models for Efficient First Order RL Axial attention in multidimensional transformers

Reference 20

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source=pdf_text observed=2026-08-03T04:03:56.851729Z digest=sha256:2ab8cb0b4fc8225d675a8a7c9d595f5a9239ca050a837d0e8560a5396aaec1ab

Observation 7894fed2-e8ea-468b-ab6a-011544c9e6a7 · outbound

This paper cites Anymal parkour: Learning agile navigation for quadrupedal robots.Science Robotics, 9(88), 2024.

Coupled Local and Global World Models for Efficient First Order RL Anymal parkour: Learning agile navigation for quadrupedal robots.Science Robotics, 9(88), 2024

Reference 21

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source=pdf_text observed=2026-08-03T04:03:56.894275Z digest=sha256:531387d8f6f5779b2afaf1c0eac8a4eda4150d6de76fb5700f1c9ddca143a9fe

Observation 3a485370-5fbd-4fd7-9c99-f655702be553 · outbound

This paper cites When to trust your model: Model-based policy optimization.Advances in neural information processing systems, 32, 2019.

Coupled Local and Global World Models for Efficient First Order RL When to trust your model: Model-based policy optimization.Advances in neural information processing systems, 32, 2019

Reference 22

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source=pdf_text observed=2026-08-03T04:03:56.943761Z digest=sha256:896ab270666f6def5a8d32908ef9fdc8f18166f9926e87bda1aef973bcc820d9

Observation 2ef21bb8-960e-4aaa-ae35-38e21a5bd598 · outbound

This paper cites An introduction to zero-order op- timization techniques for robotics.arXiv preprint arXiv:2506.22087, 2025.

Coupled Local and Global World Models for Efficient First Order RL An introduction to zero-order op- timization techniques for robotics.arXiv preprint arXiv:2506.22087, 2025

Reference 23

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source=pdf_text observed=2026-08-03T04:03:56.982842Z digest=sha256:e05ea61c8af9fb00aa1d90f6d92017a53e37ab5c5f2b9d43099d4fc8ffb78a12

Observation ccabee6a-3e02-44cf-9d84-af06f7ee6482 · outbound

This paper cites Elucidating the design space of diffusion-based genera- tive models.Advances in neural information processing systems, 35, 2022.

Coupled Local and Global World Models for Efficient First Order RL Elucidating the design space of diffusion-based genera- tive models.Advances in neural information processing systems, 35, 2022

Reference 24

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Observation df6bf141-15b5-44d8-9223-40f48002be56 · outbound

This paper cites Worldplanner: Monte carlo tree search and mpc with action-conditioned visual world models, 2025.

Coupled Local and Global World Models for Efficient First Order RL Worldplanner: Monte carlo tree search and mpc with action-conditioned visual world models, 2025

Reference 25

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Observation 7492b502-ac9d-46c7-8e19-84a0a6989370 · outbound

This paper cites Auto-encoding variational bayes.

Coupled Local and Global World Models for Efficient First Order RL Auto-encoding variational bayes

Reference 26

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Observation e0761d95-cec3-4e75-ac75-d3b31b234931 · outbound

This paper cites Model-ensemble trust-region policy optimization.

Coupled Local and Global World Models for Efficient First Order RL Model-ensemble trust-region policy optimization

Reference 27

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Observation 7fc70c44-37dd-40bb-921f-be63a0b09f29 · outbound

This paper cites Investigating Compounding Prediction Errors in Learned Dynamics Models.

Coupled Local and Global World Models for Efficient First Order RL Investigating Compounding Prediction Errors in Learned Dynamics Models

Reference 28

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source=pdf_text observed=2026-08-03T04:03:57.233115Z digest=sha256:4b1243668f96410066c8fceaad5dcb32ac9d94ab9453bb2701ad497483bd895f

Observation b6976a91-c4cc-45fc-8d59-41aaaccc3f66 · outbound

This paper cites Uncertainty-aware robotic world model makes offline model-based reinforcement learning work on real robots, 2025.

Coupled Local and Global World Models for Efficient First Order RL Uncertainty-aware robotic world model makes offline model-based reinforcement learning work on real robots, 2025

Reference 29

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source=pdf_text observed=2026-08-03T04:03:57.291026Z digest=sha256:407f0f49742e639cf1a74de66f4603aa15b7933eb759d8036f9160f133d2af53

Observation 05e9cd88-4604-4802-b826-dc9ca8633bd0 · outbound

This paper cites Lightzero: A unified benchmark for monte carlo tree search in general sequential decision scenarios.Advances in Neural Information Processing Systems, 36, 2024.

Coupled Local and Global World Models for Efficient First Order RL Lightzero: A unified benchmark for monte carlo tree search in general sequential decision scenarios.Advances in Neural Information Processing Systems, 36, 2024

Reference 30

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Observation 0386dd37-9f4a-4a13-a693-4c3b69f845db · outbound

This paper cites UniZero: Generalized and Efficient Planning with Scalable Latent World Models.

Coupled Local and Global World Models for Efficient First Order RL UniZero: Generalized and Efficient Planning with Scalable Latent World Models

Reference 31

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source=pdf_text observed=2026-08-03T04:03:57.371531Z digest=sha256:4f6e450b5d02c9025c9cd445f10470a1d9f849ec1d2ec00226a495c2641bc936

Observation 405f2d97-f906-405a-87e0-74cc059c0235 · outbound

This paper cites Mastering atari, go, chess and shogi by planning with a learned model.Nature, 588(7839):604– 609, 2020.

Coupled Local and Global World Models for Efficient First Order RL Mastering atari, go, chess and shogi by planning with a learned model.Nature, 588(7839):604– 609, 2020

Reference 32

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Observation 6356dc6a-a18e-4fb4-9367-af66dd7ac47b · outbound

This paper cites Proximal Policy Optimization Algorithms.

Coupled Local and Global World Models for Efficient First Order RL Proximal Policy Optimization Algorithms

Reference 33

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Observation 9a25fdc2-75bd-4c72-a838-842dd2ab67b2 · outbound

This paper cites Daydreamer: World models for physical robot learning.

Coupled Local and Global World Models for Efficient First Order RL Daydreamer: World models for physical robot learning

Reference 34

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Observation ffeffede-261d-4132-aaac-cf171f13466e · outbound

This paper cites Learning to combat compounding- error in model-based reinforcement learning, 2019.

Coupled Local and Global World Models for Efficient First Order RL Learning to combat compounding- error in model-based reinforcement learning, 2019

Reference 35

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source=pdf_text observed=2026-08-03T04:03:57.589844Z digest=sha256:0c16952bb7031cdb58fcc5dac7023e07ada9077f120eeac5a98169510fe81268

Observation d091cfb9-1912-45bc-a529-f29e813084e4 · outbound

This paper cites Stabilizing rein- forcement learning in differentiable multiphysics simula- tion.International Conference on Learning Representa- tions (ICLR), 2025.

Coupled Local and Global World Models for Efficient First Order RL Stabilizing rein- forcement learning in differentiable multiphysics simula- tion.International Conference on Learning Representa- tions (ICLR), 2025

Reference 36

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Observation 4445579d-62d0-436d-8a35-942cc6bcfa18 · outbound

This paper cites Rank2reward: Learning shaped reward functions from passive video.

Coupled Local and Global World Models for Efficient First Order RL Rank2reward: Learning shaped reward functions from passive video

Reference 37

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Observation 306fc9f4-dff9-4422-a5e2-76d025c93d45 · outbound

This paper cites Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn.

Coupled Local and Global World Models for Efficient First Order RL Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn

Reference 38

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Observation 087ca447-38d7-4023-9947-fc8b91fecbf6 · outbound

This paper cites Sim-to-real transfer in deep reinforcement learning for robotics: a survey.

Coupled Local and Global World Models for Efficient First Order RL Sim-to-real transfer in deep reinforcement learning for robotics: a survey

Reference 39

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Observation 6799c458-a3c1-45b7-a582-e722a8acc9be · outbound

This paper cites Robot Parkour Learning.

Coupled Local and Global World Models for Efficient First Order RL Robot Parkour Learning

Reference 40

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Pith citing papers

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