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Paper Citation Record · LEDGER

Exploratory Diffusion Model for Unsupervised Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 0 inbound Pith citation observations for arXiv:2502.07279.

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

pith.paper-citation-record.v1
2502.07279 v2

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

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measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

70 of 70 outbound references displayed

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External citation measurements

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Outbound references

Observation 717804df-2c64-4226-83eb-f028019cddf1 · outbound

This paper cites Deep reinforcement learning at the edge of the statistical precipice.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Deep reinforcement learning at the edge of the statistical precipice

Reference 1

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Observation 1186c289-b8cd-485a-8f62-13bd7c4d93da · outbound

This paper cites Tenenbaum, Tommi S.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Tenenbaum, Tommi S

Reference 2

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Observation 52013a13-f6fb-4638-a35d-f2950327fed6 · outbound

This paper cites Diffusion for World Modeling: Visual Details Matter in Atari.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Diffusion for World Modeling: Visual Details Matter in Atari

Reference 3

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Observation 1c04bd1b-5546-4893-bbf7-dd6bb3d3540b · outbound

This paper cites Random polytopes, convex bodies, and approximation.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Random polytopes, convex bodies, and approximation

Reference 4

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Observation b5f258a2-ebde-445b-a142-3e292e93ebe1 · outbound

This paper cites Constrained Ensemble Exploration for Unsupervised Skill Discovery.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Constrained Ensemble Exploration for Unsupervised Skill Discovery

Reference 5

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Observation 8f7e1583-b62b-47d7-81c5-d76e8d9c4e00 · outbound

This paper cites Exploration by random network distillation.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Exploration by random network distillation

Reference 6

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Observation 9ce0c823-a0ac-4384-888a-ad82f96ec597 · outbound

This paper cites Explore, discover and learn: Unsupervised discovery of state-covering skills.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Explore, discover and learn: Unsupervised discovery of state-covering skills

Reference 7

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Observation 4f41f3bf-524c-4d7d-b5ec-4ce13f26c927 · outbound

This paper cites DIME:Diffusion-Based Maximum Entropy Reinforcement Learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning DIME:Diffusion-Based Maximum Entropy Reinforcement Learning

Reference 8

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Observation 749b2cf6-799b-4bcd-ad89-0bd4e41b92a5 · outbound

This paper cites Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

Reference 9

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Observation 193876ed-7802-427e-94db-c299f5ddd8df · outbound

This paper cites Simple Hierarchical Planning with Diffusion.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Simple Hierarchical Planning with Diffusion

Reference 10

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Observation 249abdf1-5bc1-4cb2-b12b-91895894bbe9 · outbound

This paper cites Offline reinforcement learning via high-fidelity generative behavior modeling.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Offline reinforcement learning via high-fidelity generative behavior modeling

Reference 11

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Observation b00d0d5a-5bcb-4ec0-81b1-cf47f5dbac27 · outbound

This paper cites Aligning Diffusion Behaviors with Q-functions for Efficient Continuous Control.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Aligning Diffusion Behaviors with Q-functions for Efficient Continuous Control

Reference 12

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Observation 20988d13-2fcd-4b7a-9bad-507db77e9192 · outbound

This paper cites Diffusion policy: Visuomotor policy learning via action diffusion.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Diffusion policy: Visuomotor policy learning via action diffusion

Reference 13

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Observation b8dca37e-e625-40fc-98d4-dffeb92cb3b0 · outbound

This paper cites Diffusion Posterior Sampling for General Noisy Inverse Problems.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Diffusion Posterior Sampling for General Noisy Inverse Problems

Reference 14

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Observation f1b9aff1-ba8b-4410-81e3-dbf0334efd1a · outbound

This paper cites Diffusion models beat gans on image synthesis.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Diffusion models beat gans on image synthesis

Reference 15

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Observation 5bc01a3b-00f9-4a54-837c-210713bbf508 · outbound

This paper cites Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning

Reference 16

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Observation cba315ae-fa8a-4bee-abeb-1f10bb41e019 · outbound

This paper cites Diversity is all you need: Learning skills without a reward function.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Diversity is all you need: Learning skills without a reward function

Reference 17

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Observation 54de6cbc-71f0-46b0-a13f-32ea678baa77 · outbound

This paper cites The information geometry of unsupervised reinforcement learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning The information geometry of unsupervised reinforcement learning

Reference 18

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Observation e6ded4df-fa16-481d-ac4b-24d104174cae · outbound

This paper cites Reinforcement learning with deep energy-based policies.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Reinforcement learning with deep energy-based policies

Reference 19

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Observation 8b9f12ab-cea0-4ef4-984c-bbdd091494d1 · outbound

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

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 20

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Observation 1803d2cc-0e49-4e91-8a24-4828acd0e416 · outbound

This paper cites IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies

Reference 21

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Observation d90fbec2-cc75-4562-a0d7-519a7e761017 · outbound

This paper cites Diffusion model is an effective planner and data synthesizer for multi-task reinforcement learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Diffusion model is an effective planner and data synthesizer for multi-task reinforcement learning

Reference 22

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Observation 65d6ffed-8431-4bc1-a236-6b8da9905ac9 · outbound

This paper cites Denoising diffusion probabilistic models.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Denoising diffusion probabilistic models

Reference 23

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Observation 09ba4fbd-d15c-448d-86d4-586560b38a47 · outbound

This paper cites Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning

Reference 24

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Observation 64ea5b7d-a4d1-46a6-b3d7-bc072f27085f · outbound

This paper cites Planning with diffusion for flexible behavior synthesis.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Planning with diffusion for flexible behavior synthesis

Reference 25

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Observation 0c81c733-6952-4b95-b4ef-2d8cb7a0c569 · outbound

This paper cites Efficient diffusion policies for offline reinforcement learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Efficient diffusion policies for offline reinforcement learning

Reference 26

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Observation 3204f4ed-570d-404d-a18a-3d0ca8e4e2a1 · outbound

This paper cites Unsupervised skill discovery with bottleneck option learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Unsupervised skill discovery with bottleneck option learning

Reference 27

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Observation edc71b78-a83a-4795-9c54-6c4638cda4b5 · outbound

This paper cites Offline reinforcement learning with implicit q-learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Offline reinforcement learning with implicit q-learning

Reference 28

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Observation 73b8f274-5633-4882-8931-202c37755d65 · outbound

This paper cites Unsuper- vised reinforcement learning with contrastive intrinsic control.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Unsuper- vised reinforcement learning with contrastive intrinsic control

Reference 29

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Observation cf259c37-eb98-47a3-ad23-3cb9c6052bb5 · outbound

This paper cites Urlb: Unsupervised reinforcement learning benchmark.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Urlb: Unsupervised reinforcement learning benchmark

Reference 30

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Observation b4104a14-fb61-4278-a931-3d2289529bc0 · outbound

This paper cites Efficient Exploration via State Marginal Matching.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Efficient Exploration via State Marginal Matching

Reference 31

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source=pdf_text observed=2026-08-08T13:21:18.274465Z digest=sha256:c40d649552dacf0259244f8186692923bae484edd5fe72220db73909f0e184e2

Observation 5c091b6d-b0e9-499a-b58b-8156c8a0d9ec · outbound

This paper cites Hierarchical diffusion for offline decision making.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Hierarchical diffusion for offline decision making

Reference 32

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Observation 5d47d839-47e1-4e91-8bf6-b317946c851b · outbound

This paper cites Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradient.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradient

Reference 33

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Observation 6c2cff4f-45cc-4e34-84b6-a9c51d4e44ad · outbound

This paper cites Adaptdiffuser: Diffusion models as adaptive self-evolving planners.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Adaptdiffuser: Diffusion models as adaptive self-evolving planners

Reference 34

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Observation bb26dcbb-b4dd-4744-8451-210dee826867 · outbound

This paper cites Continuous control with deep reinforcement learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Continuous control with deep reinforcement learning

Reference 35

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Observation acacfe4a-278d-49ad-9b0e-3b4e0ab72907 · outbound

This paper cites Aps: Active pretraining with successor features.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Aps: Active pretraining with successor features

Reference 36

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Observation 6e3ab296-d29d-45aa-8575-4f2845740779 · outbound

This paper cites Behavior from the void: Unsupervised active pre-training.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Behavior from the void: Unsupervised active pre-training

Reference 37

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raw_fallback, observed 2026-08-08T13:21:19.226391Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-08T13:21:18.307110Z digest=sha256:9ca02a502343b51b0d5f6ff83f5a348cd302f1b441233458e7a05361813a14ea

Observation 436e4d5e-93f0-458f-9091-e6ccdef8eb7a · outbound

This paper cites Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning

Reference 38

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source=pdf_text observed=2026-08-08T13:21:18.313075Z digest=sha256:9bec3ef70981ab0eaa47bea9fcedfe7efaee2e52d004c6370be0db272e71687a

Observation 43d3f187-783d-4d0c-bf6d-39429be652df · outbound

This paper cites Contrastive energy prediction for exact energy-guided diffusion sampling in offline reinforcement learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Contrastive energy prediction for exact energy-guided diffusion sampling in offline reinforcement learning

Reference 39

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source=pdf_text observed=2026-08-08T13:21:18.318224Z digest=sha256:430b3688f0a38c7aa0ace3c888842ee432683cad664fdf32dd14a6ae03d43139

Observation d6dd9ac4-0c13-4e8e-ba70-a58255ea0506 · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

Reference 40

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source=pdf_text observed=2026-08-08T13:21:18.323013Z digest=sha256:8be9bfa84eac9e02f485c3032a5d7aa2cb9d6bb4daf52401f6d591b4be7dff0a

Observation cab513be-a52c-4aa6-841e-378ec63bd8f9 · outbound

This paper cites Synthetic experience replay.Advances in Neural Information Processing Systems, 36, 2024.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Synthetic experience replay.Advances in Neural Information Processing Systems, 36, 2024

Reference 41

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raw_fallback, observed 2026-08-08T13:21:19.182147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T13:21:18.328698Z digest=sha256:71b372084df5792d7084fbf226c08dfef4173df6b93aea2f92cb1e753999da33

Observation e1fcfedc-9fa5-464d-813c-f463941d62e6 · outbound

This paper cites Efficient Online Reinforcement Learning for Diffusion Policy.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Efficient Online Reinforcement Learning for Diffusion Policy

Reference 42

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source=pdf_text observed=2026-08-08T13:21:18.333364Z digest=sha256:b50d7de287c7139f6663b6ebac7783ebbd8d4f983e93eba42437658f8112a43c

Observation 4c5de78b-2d75-439a-83d5-9af270e71cc1 · outbound

This paper cites Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone

Reference 43

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source=pdf_text observed=2026-08-08T13:21:18.339718Z digest=sha256:be10476d2e8d10b8a43e3b67db69e282060af70985e0d37dfc5350537b1102b7

Observation ad88db33-c4b8-4834-933b-e230cbf6614d · outbound

This paper cites Curiosity-driven exploration via latent bayesian surprise.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Curiosity-driven exploration via latent bayesian surprise

Reference 44

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raw_fallback, observed 2026-08-08T13:21:19.166694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T13:21:18.344637Z digest=sha256:16230704e22088aa6409cfd387a7346464e5131c713fba6f32bf60fe78aaf24a

Observation e36c7829-ae27-43f0-8d40-9e40197bdf24 · outbound

This paper cites Lipschitz-constrained unsupervised skill discovery.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Lipschitz-constrained unsupervised skill discovery

Reference 45

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raw_fallback, observed 2026-08-08T13:21:19.151235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T13:21:18.349181Z digest=sha256:9d21e085548f7a8c2b4670b47c155c9867981c0e47da00ed6c5d8b14e2d811be

Observation 25988992-dc49-4b2d-b2c8-54d202233e5e · outbound

This paper cites METRA: Scalable Unsupervised RL with Metric-Aware Abstraction.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning METRA: Scalable Unsupervised RL with Metric-Aware Abstraction

Reference 46

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source=pdf_text observed=2026-08-08T13:21:18.353849Z digest=sha256:b20435eb24b4f45be0dd4712455e6e804b0407190e2cc0d3986da95e4992c897

Observation de555d34-5ec6-4044-b0ab-c4e20ecf47c2 · outbound

This paper cites Curiosity-driven exploration by self-supervised prediction.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Curiosity-driven exploration by self-supervised prediction

Reference 47

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raw_fallback, observed 2026-08-08T13:21:19.135131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T13:21:18.358862Z digest=sha256:f619ee950bdf760cc7256a9d85deaf2f4722669adcb2d76dde6d0c67ff00c04e

Observation 360a91dc-9430-4d88-ad63-b63cf128d0fd · outbound

This paper cites Self-supervised exploration via disagreement.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Self-supervised exploration via disagreement

Reference 48

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raw_fallback, observed 2026-08-08T13:21:19.118151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T13:21:18.363809Z digest=sha256:35612d0bdad6e99b546cc837ea6ae856cab03b7c23b85e9ebb7b02de41cbc3b2

Observation b957b3af-87ce-4b1b-b3ef-1e63cc384755 · outbound

This paper cites Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

Reference 49

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source=pdf_text observed=2026-08-08T13:21:18.368484Z digest=sha256:dfd25c711a65827500d3980b76788185ae9456682987ac5e220c20045828aa31

Observation 42042865-f0ce-403b-a6f5-260f47240610 · outbound

This paper cites Learning a Diffusion Model Policy from Rewards via Q-Score Matching.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Learning a Diffusion Model Policy from Rewards via Q-Score Matching

Reference 50

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source=pdf_text observed=2026-08-08T13:21:18.374992Z digest=sha256:170e11e512554dbd39c574960e4a75106f42dc8909c56ebd3ea48b3a0d31320f

Observation 06931fde-8a40-45aa-87d0-7d1de4e1d46c · outbound

This paper cites Diffusion Policy Policy Optimization.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Diffusion Policy Policy Optimization

Reference 51

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source=pdf_text observed=2026-08-08T13:21:18.380367Z digest=sha256:f07a39b5f989bf624b774eeefa0df8956608e0ab3fceb3ff13dd0372d647320e

Observation 2707d75e-063d-4aec-be49-31b33b7b274d · outbound

This paper cites Photorealistic text-to- image diffusion models with deep language understanding.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Photorealistic text-to- image diffusion models with deep language understanding

Reference 52

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source=pdf_text observed=2026-08-08T13:21:18.385361Z digest=sha256:138a37c6a8d75b3196ebd30bd2bcf2585d0bca5facb2cc7eab2df1c225f3fd0d

Observation db3205d2-3abc-442f-97a3-48c031397268 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 53

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source=pdf_text observed=2026-08-08T13:21:18.390208Z digest=sha256:c4b8954ea9bad527a86400a92e199e9173731138b882dd29b3207550566134a2

Observation cbbf8218-1a96-4e75-b11d-10589a6f930c · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Deep unsupervised learning using nonequilibrium thermodynamics

Reference 54

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source=pdf_text observed=2026-08-08T13:21:18.395721Z digest=sha256:5c45dfd3562de87a64b8d4fde15d67d2198b2607ff64bb6fb1803e56c40f0d2f

Observation 9c1c4d90-8598-49f0-8f25-d4866102963a · outbound

This paper cites Denoising diffusion implicit models.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Denoising diffusion implicit models

Reference 55

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source=pdf_text observed=2026-08-08T13:21:18.400244Z digest=sha256:b24429c9aa7a8db08d3ebe0d0b5b9b187f8f9aef03f873be1f50d3e0a110a790

Observation 413bb471-9086-4afb-904a-9a6070a93c95 · outbound

This paper cites Score-based generative modeling through stochastic differential equations.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Score-based generative modeling through stochastic differential equations

Reference 56

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source=pdf_text observed=2026-08-08T13:21:18.404894Z digest=sha256:f551dc551242ec56dbf8d12fd3855fe43f096af9672271bd189984082053ace9

Observation 0b3437f2-eb5b-4c53-89d8-2a520ae2f4b7 · outbound

This paper cites Reinforcement learning: An introduction.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Reinforcement learning: An introduction

Reference 57

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source=pdf_text observed=2026-08-08T13:21:18.409791Z digest=sha256:684fc273d7b3ff63de138ef75d1dd470bd577b94bbafe20bb8c29783c5ac4b4b

Observation 55ee6be8-6836-40f9-a899-c5f22e3d4788 · outbound

This paper cites DeepMind Control Suite.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning DeepMind Control Suite

Reference 58

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source=pdf_text observed=2026-08-08T13:21:18.414534Z digest=sha256:4174b8d46e883b22dad080cb5ada291c7ebe6070f87d0b07f579308417fc61e3

Observation bce0a378-af0f-4c70-a587-9312f03ea012 · outbound

This paper cites Prioritized Generative Replay.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Prioritized Generative Replay

Reference 59

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source=pdf_text observed=2026-08-08T13:21:18.420119Z digest=sha256:7de16f53730590ef9f09f4eb0525466b5ff0d5713243ecb658daac71c8b7c5f8

Observation 6594c9c2-0ac2-47fb-9fb0-2a70427a63b8 · outbound

This paper cites Diffusion policies as an expressive policy class for offline reinforcement learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Diffusion policies as an expressive policy class for offline reinforcement learning

Reference 60

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source=pdf_text observed=2026-08-08T13:21:18.424969Z digest=sha256:e45685c6c9c585e7c35b69a578d3e17cf004306972ef51cb2319609dc0fc2087

Observation 0198e1c6-8411-45ae-951a-24aca56b6b25 · outbound

This paper cites A problem in geometric probability.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning A problem in geometric probability

Reference 61

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raw_fallback, observed 2026-08-08T13:21:19.039086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T13:21:18.429537Z digest=sha256:66040fcb78ea066fac14ce7c5115f6d92b43eba9a7209496f2b8ead15bc46b35

Observation 5944f1a6-f8ea-44a9-90e7-e9a2249fdadb · outbound

This paper cites Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration

Reference 62

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source=pdf_text observed=2026-08-08T13:21:18.434308Z digest=sha256:c03e4e5e0f65f6ba185e7258d2ca9f5efee62948a6e12926eacf0d0c23249d65

Observation 3f30d08a-8ca5-447b-b349-8d05edb0bc5b · outbound

This paper cites Policy Representation via Diffusion Probability Model for Reinforcement Learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Policy Representation via Diffusion Probability Model for Reinforcement Learning

Reference 63

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source=pdf_text observed=2026-08-08T13:21:18.438947Z digest=sha256:33632d32652178e0c4a7f1ce09f3f674884b25fe6c4d0af24b5f35f8ed7d3c1d

Observation 78c1c545-d060-40a5-be76-0c7b9837b1b8 · outbound

This paper cites Behavior contrastive learning for unsupervised skill discovery.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Behavior contrastive learning for unsupervised skill discovery

Reference 64

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raw_fallback, observed 2026-08-08T13:21:19.022950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T13:21:18.443810Z digest=sha256:0778de8446bbda006519c27edb6e77b122df73f1e24313c6bc484ac1c634808e

Observation a5ec26a0-6739-42d7-9d04-108188dd472f · outbound

This paper cites Peac: Unsupervised pre-training for cross-embodiment reinforcement learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Peac: Unsupervised pre-training for cross-embodiment reinforcement learning

Reference 65

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raw_fallback, observed 2026-08-08T13:21:19.006929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T13:21:18.448752Z digest=sha256:92c89106314a9a77fd3e71db96452a2e37795812fd0d58ff7df5ae592c40abe4

Observation 6ba8d291-d21f-4541-8de4-667a1bc26e33 · outbound

This paper cites Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk

Reference 66

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source=pdf_text observed=2026-08-08T13:21:18.453588Z digest=sha256:49773ab2997d34ee30f4cfb042ba3f282ba18bbda295d30cb76b314be5db693b

Observation 5376eb95-3c8f-4f54-84ab-3ed8f27c2477 · outbound

This paper cites Automatic intrinsic reward shaping for exploration in deep reinforcement learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Automatic intrinsic reward shaping for exploration in deep reinforcement learning

Reference 67

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raw_fallback, observed 2026-08-08T13:21:18.989867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T13:21:18.459069Z digest=sha256:7883bed7b73b41fe1b7399d6e2df0c1a35a6e15b2179b0a4fae3a7430d2f58ad

Observation 97a9345c-5642-4da6-94fc-5352cf84b381 · outbound

This paper cites EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model

Reference 68

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source=pdf_text observed=2026-08-08T13:21:18.464264Z digest=sha256:6f97eb9c435cbefaa56863330b41b4afebaa2d4a3866635cc5fbb884bc63c961

Observation 7d7c98b1-be79-47c8-b37e-c988112b88fa · outbound

This paper cites A mixture of surprises for unsupervised reinforcement learning.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning A mixture of surprises for unsupervised reinforcement learning

Reference 69

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raw_fallback, observed 2026-08-08T13:21:18.973702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T13:21:18.469375Z digest=sha256:d82ace3b9adf24b2109ee817f4d77051e1954c53c98c1d45af383765722733d9

Observation a33c2e8f-595f-448c-ba50-fecddc257fad · outbound

This paper cites Diffusion Models for Reinforcement Learning: A Survey.

Exploratory Diffusion Model for Unsupervised Reinforcement Learning Diffusion Models for Reinforcement Learning: A Survey

Reference 70

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no resolver link, observed 2026-08-08T13:21:18.474336Z

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source=pdf_text observed=2026-08-08T13:21:18.474336Z digest=sha256:7a0649ac392d7e579cae8e4504d3597f5b08e50d618b313e9f09afb142aa891d

Pith citing papers

No inbound Pith citation observations are available.