Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF | Paper Citation Record

Observation 1fe76140-f202-44d1-b07d-16bf1467641a · outbound

This paper cites Hindsight Experience Replay.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Hindsight Experience Replay

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.909974Z

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Observation e304a2af-3e64-4c16-9c44-bb570395a8ca · outbound

This paper cites 133011, 3, 7, 8, 12.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF 133011, 3, 7, 8, 12

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T02:25:56.026565Z

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Observation ad5a402c-9aaa-462e-bb2b-1a204b96b0e6 · outbound

This paper cites DSPO: Direct Semantic Preference Optimization for Real-World Image Super-Resolution.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF DSPO: Direct Semantic Preference Optimization for Real-World Image Super-Resolution

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.845525Z

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Observation faf7892d-89e3-4a22-8b06-16ddceeab3c7 · outbound

This paper cites MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.876895Z

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Observation 2fc94259-9502-4c79-8de2-e8f5b0dec446 · outbound

This paper cites In: Proceedings of Robotics: Science and Systems (RSS) (2023) 2.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF In: Proceedings of Robotics: Science and Systems (RSS) (2023) 2

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T02:25:56.016587Z

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Observation 20f0dcde-2523-43e2-83b4-599e538aedfd · outbound

This paper cites Deep reinforcement learning from human preferences.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Deep reinforcement learning from human preferences

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.932859Z

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Observation 1c41d852-001c-4642-8b42-97bc12a136e5 · outbound

This paper cites Directly Fine-Tuning Diffusion Models on Differentiable Rewards.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Directly Fine-Tuning Diffusion Models on Differentiable Rewards

Reference 7

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T02:25:55.930343Z

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Observation ec6b81b7-a673-4974-8039-af0e933faa18 · outbound

This paper cites DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.947272Z

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Observation 325b6d17-b4e2-4aa9-89b5-3c411dbdab38 · outbound

This paper cites Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.912565Z

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Observation 4bd34cc9-7b99-4330-b53a-b71af152eced · outbound

This paper cites TempFlow-GRPO: When Timing Matters for GRPO in Flow Models.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF TempFlow-GRPO: When Timing Matters for GRPO in Flow Models

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T02:25:55.870955Z

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Observation d3bf136e-8151-4f30-b16e-adef5d24e515 · outbound

This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.831004Z

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Observation c31117ed-4527-44e5-95a5-b99a77ec8b08 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Denoising Diffusion Probabilistic Models

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.937670Z

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Observation f375009b-90de-4073-8196-d8853fcb7f82 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF LoRA: Low-Rank Adaptation of Large Language Models

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.865485Z

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Observation 9fd710a6-780b-4245-bb15-a54709896175 · outbound

This paper cites Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.902473Z

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Observation 4c63c6ed-0eac-4cfe-9f67-2f0f138a3667 · outbound

This paper cites PatchDPO: Patch-level DPO for Finetuning-free Personalized Image Generation.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF PatchDPO: Patch-level DPO for Finetuning-free Personalized Image Generation

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.839812Z

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Observation 1ce6d9bd-f0e1-424a-8712-342fe507c107 · outbound

This paper cites TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations

Reference 16

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Observation f0d7ae0d-6bd3-4e43-b65f-4847ff0c983d · outbound

This paper cites Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable Latent-Space Surrogate Reward.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable Latent-Space Surrogate Reward

Reference 17

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Observation c1b1dd6e-26fd-4bf5-aedf-6fd2ee8daacf · outbound

This paper cites Branchgrpo: Stable and efficient grpo with structured branching in diffusion models.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Branchgrpo: Stable and efficient grpo with structured branching in diffusion models

Reference 18

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Observation 95e0288b-a9ed-4d78-9dfc-95742979b630 · outbound

This paper cites an unresolved cited work.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Unresolved cited work

Reference 19

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Observation 527924e1-c108-4dd9-a2b5-9f871f963f3b · outbound

This paper cites Continuous control with deep reinforcement learning.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Continuous control with deep reinforcement learning

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T02:25:55.935385Z

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Observation 372b9069-a4e0-49ee-8396-2bfb6cae3fe0 · outbound

This paper cites Flow-GRPO: Training Flow Matching Models via Online RL.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Flow-GRPO: Training Flow Matching Models via Online RL

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.899907Z

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Observation 35f1aebb-ca82-444f-8f49-795842f1c964 · outbound

This paper cites Synthetic Experience Replay.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Synthetic Experience Replay

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.942485Z

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Observation 82a6cc54-75ae-400f-aa8f-12b51ae1a4d5 · outbound

This paper cites Dual-Process Image Generation.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Dual-Process Image Generation

Reference 23

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Observation db1ce687-aace-4e05-8e87-077bb8278092 · outbound

This paper cites an unresolved cited work.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Unresolved cited work

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-09T02:25:55.842469Z

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Observation c9c3d08e-3131-45c3-820c-7cceeb301579 · outbound

This paper cites Do text-free diffusion models learn discriminative visual representations?.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Do text-free diffusion models learn discriminative visual representations?

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.914949Z

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Observation c79984dd-eab4-4ca8-8b7e-107df3a0cbd1 · outbound

This paper cites GPT-4 Technical Report.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF GPT-4 Technical Report

Reference 26

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Observation 7c6223df-d4d5-4464-85ea-da6e1faa76cd · outbound

This paper cites Training language models to follow instructions with human feedback.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Training language models to follow instructions with human feedback

Reference 27

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Observation f0b870ad-7af4-42ae-b76e-37848e72aaf4 · outbound

This paper cites Reinforcement learning by reward-weighted regression for operational space control.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Reinforcement learning by reward-weighted regression for operational space control

Reference 28

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Observation 6a2bf9f6-84dc-4e57-9ede-0145bc00a5dc · outbound

This paper cites Hard examples are all you need: Maximizing grpo post-training under annotation budgets.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Hard examples are all you need: Maximizing grpo post-training under annotation budgets

Reference 29

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Observation 2f03f31d-7a68-4176-a776-c54ed1bf771c · outbound

This paper cites Video Diffusion Alignment via Reward Gradients.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Video Diffusion Alignment via Reward Gradients

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.922322Z

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Observation eac7afe5-338e-4119-b7d7-9c0af4a5c505 · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 31

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Observation dc409362-ad24-4ba8-be55-e43b05569ebd · outbound

This paper cites an unresolved cited work.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Unresolved cited work

Reference 32

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Observation bc044944-4a3d-43b5-a8ea-32f03a7eb92a · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR) (2022) 7.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR) (2022) 7

Reference 33

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Observation 07e2a8de-2d17-4a3e-a807-cd8bff3b5774 · outbound

This paper cites Prioritized Experience Replay.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Prioritized Experience Replay

Reference 34

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Observation 9147be8d-01af-4b19-ae1d-93fede2baa8f · outbound

This paper cites an unresolved cited work.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Unresolved cited work

Reference 35

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Observation 2b0dea72-f54f-49a0-9830-0a0fdc3f8114 · outbound

This paper cites Trust Region Policy Optimization.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Trust Region Policy Optimization

Reference 36

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Observation d64aa1d4-622c-4a35-a9b3-e0c503653fdc · outbound

This paper cites Proximal Policy Optimization Algorithms.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Proximal Policy Optimization Algorithms

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.836731Z

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Observation 48fe61e6-bdc9-4fd8-892c-99ccbf491efe · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.854539Z

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Observation 32a72b49-ff3e-48c1-b5b7-a46db07da39d · outbound

This paper cites RL's Razor: Why Online Reinforcement Learning Forgets Less.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF RL's Razor: Why Online Reinforcement Learning Forgets Less

Reference 39

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Observation a55cffbf-3e07-48f3-be56-bef4b0ec943e · outbound

This paper cites Training Region-based Object Detectors with Online Hard Example Mining.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Training Region-based Object Detectors with Online Hard Example Mining

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.848620Z

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Observation eaece74c-41df-4db0-a57e-c698a7874e27 · outbound

This paper cites Deep Unsupervised Learning using Nonequilibrium Thermodynamics.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.834022Z

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Observation d7dd12aa-10d5-4484-a49e-bcbdb5baa0e4 · outbound

This paper cites Denoising Diffusion Implicit Models.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Denoising Diffusion Implicit Models

Reference 42

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Observation dd3d5b43-cf21-4969-a33b-b6cf14d54ed2 · outbound

This paper cites MIT press, ??? (2018).

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF MIT press, ??? (2018)

Reference 43

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Observation 050d4b37-ae3a-4772-964a-9aaec3a18bbc · outbound

This paper cites Diffusion Model Alignment Using Direct Preference Optimization.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Diffusion Model Alignment Using Direct Preference Optimization

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.897454Z

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Observation a8fd6701-d236-4a4f-8fdc-070b8d29c6bc · outbound

This paper cites an unresolved cited work.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-07-09T02:25:56.019952Z

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Observation c706126f-6539-4203-845b-a6a06bf6d52d · outbound

This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 46

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T02:25:55.892309Z

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Observation f6805c19-0029-42c5-a7f3-160d2e405fc9 · outbound

This paper cites Data Retrieval with Importance Weights for Few-Shot Imitation Learning.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Data Retrieval with Importance Weights for Few-Shot Imitation Learning

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.868395Z

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Observation 574c806b-1fe1-47ff-bf13-81f284638c4b · outbound

This paper cites Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.944838Z

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Observation b28ff67b-39ac-4803-bf5e-ec10c7a3ce4c · outbound

This paper cites Advances in Neural Information Processing Systems 36, 15903–15935 (2023) 7.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Advances in Neural Information Processing Systems 36, 15903–15935 (2023) 7

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T02:25:56.018386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation eabf51b1-b9d6-42ce-81ef-a31b19df1a97 · outbound

This paper cites DanceGRPO: Unleashing GRPO on Visual Generation.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF DanceGRPO: Unleashing GRPO on Visual Generation

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.860040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a27aef16-94fb-4308-9dee-1966183073fe · outbound

This paper cites Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.924899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3bb1a0b2-44cd-4107-b72b-b8c9908e4275 · outbound

This paper cites Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.919756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b31d0fa8-79a1-408e-aa28-05728b57cc10 · outbound

This paper cites SIPO: Stabilized and Improved Preference Optimization for Aligning Diffusion Models.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF SIPO: Stabilized and Improved Preference Optimization for Aligning Diffusion Models

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.851618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7cb99871-101a-4543-9a56-60f6cd2d3c6a · outbound

This paper cites Energy-Based Hindsight Experience Prioritization.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF Energy-Based Hindsight Experience Prioritization

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:25:55.917249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6054a268-b56d-4914-810b-d30d2848c609 · outbound

This paper cites arXiv preprint arXiv:2510.01982 (2025) 3.

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF arXiv preprint arXiv:2510.01982 (2025) 3

Reference 55

Resolution: metadata mismatch; arxiv_id, observed 2026-07-09T02:25:55.873580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Reference resolution

Outbound references

Pith citing papers