Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning | Paper Citation Record

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This paper cites Sutton and Andrew G.

Reference 1

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This paper cites Reinforcement learning can be more efficient with multiple rewards.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Reinforcement learning can be more efficient with multiple rewards

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This paper cites Learning Agile Robotic Locomotion Skills by Imitating Animals.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Learning Agile Robotic Locomotion Skills by Imitating Animals

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Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Deep object-centric represen- tations for generalizable robot learning

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Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning The ingredients of real world robotic reinforcement learning

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This paper cites Preference Transformer: Modeling Human Preferences using Transformers for RL.

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This paper cites Fine-Tuning Language Models from Human Preferences.

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

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Training language models to follow instructions with human feedback

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This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Reference 10

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This paper cites Active Preference-Based Learning of Reward Functions.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Active Preference-Based Learning of Reward Functions

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Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Deep Reinforcement Learning from Human Preferences

Reference 12

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This paper cites A Survey of Preference-Based Reinforcement Learning Methods.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning A Survey of Preference-Based Reinforcement Learning Methods

Reference 13

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This paper cites Few-shot preference learning for human-in-the-loop RL.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Few-shot preference learning for human-in-the-loop RL

Reference 16

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This paper cites Bradley Knox, and Dorsa Sadigh.

Reference 17

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This paper cites Inverse Preference Learning: Preference-based RL without a Reward Function.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Inverse Preference Learning: Preference-based RL without a Reward Function

Reference 18

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Observation 327d9590-55cc-4c53-9ddb-7df41ee30b9b · outbound

This paper cites Direct Preference-based Policy Optimization without Reward Modeling.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Direct Preference-based Policy Optimization without Reward Modeling

Reference 19

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Observation 364218c0-d7c7-44f6-a6e0-26c1e2dd405c · outbound

This paper cites Beyond Reward: Offline Preference-guided Policy Optimization.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Beyond Reward: Offline Preference-guided Policy Optimization

Reference 20

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

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 21

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This paper cites Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning

Reference 22

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This paper cites Rethinking reward modeling in preference-based large language model alignment.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Rethinking reward modeling in preference-based large language model alignment

Reference 23

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This paper cites Generalized preference optimization: A unified approach to offline alignment.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Generalized preference optimization: A unified approach to offline alignment

Reference 24

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This paper cites Nash learning from human feedback.

Reference 25

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This paper cites A general theoretical paradigm to understand learning from human preferences.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning A general theoretical paradigm to understand learning from human preferences

Reference 26

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This paper cites Online Iterative Reinforcement Learning from Human Feedback with General Preference Model.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Online Iterative Reinforcement Learning from Human Feedback with General Preference Model

Reference 27

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This paper cites Intransitivity of preferences.

Reference 28

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This paper cites RIME: robust preference-based reinforcement learning with noisy preferences.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning RIME: robust preference-based reinforcement learning with noisy preferences

Reference 30

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This paper cites AlpacaFarm: A Sim- ulation Framework for Methods that Learn from Human Feedback.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning AlpacaFarm: A Sim- ulation Framework for Methods that Learn from Human Feedback

Reference 31

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This paper cites Reward model ensembles help mitigate overoptimization.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Reward model ensembles help mitigate overoptimization

Reference 32

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This paper cites B-pref: Benchmarking preference- based reinforcement learning.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning B-pref: Benchmarking preference- based reinforcement learning

Reference 33

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This paper cites Rethinking decision transformer via hierarchical reinforcement learning.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Rethinking decision transformer via hierarchical reinforcement learning

Reference 34

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This paper cites WHEN SHOULD WE PREFER DECISION TRANSFORMERS FOR OFFLINE REINFORCE- MENT LEARNING? 2024.

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Reference 35

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This paper cites OFFLINE REINFORCEMENT LEARNING WITH IMPLICIT Q-LEARNING.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning OFFLINE REINFORCEMENT LEARNING WITH IMPLICIT Q-LEARNING

Reference 36

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This paper cites Preference Alignment with Flow Matching.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Preference Alignment with Flow Matching

Reference 37

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This paper cites Flow to better: Offline preference-based reinforcement learning via preferred trajectory generation.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Flow to better: Offline preference-based reinforcement learning via preferred trajectory generation

Reference 38

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This paper cites Denoising Implicit Feedback for Recommendation.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Denoising Implicit Feedback for Recommendation

Reference 39

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This paper cites mixup: BEYOND EMPIRICAL RISK MINIMIZATION.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning mixup: BEYOND EMPIRICAL RISK MINIMIZATION

Reference 40

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This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Co-teaching: Robust training of deep neural networks with extremely noisy labels

Reference 41

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This paper cites Does label smoothing mitigate label noise? In Proceedings of the 37th International Conference on Machine Learning, volume 119 of ICML’20, pages 6448–6458.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Does label smoothing mitigate label noise? In Proceedings of the 37th International Conference on Machine Learning, volume 119 of ICML’20, pages 6448–6458

Reference 42

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This paper cites Learning from noisy labels with deep neural networks: A survey.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Learning from noisy labels with deep neural networks: A survey

Reference 43

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This paper cites Attention is All you Need.

Reference 44

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This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning A Simple Framework for Contrastive Learning of Visual Representations

Reference 45

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This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Reference 46

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This paper cites D4RL: Building Better Benchmarks for Offline Reinforcement Learning.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning D4RL: Building Better Benchmarks for Offline Reinforcement Learning

Reference 47

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This paper cites URL https://www.gymlibrary.dev/environments/ mujoco/index.html.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning URL https://www.gymlibrary.dev/environments/ mujoco/index.html

Reference 48

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This paper cites Offlinerl-kit: An elegant pytorch offline reinforcement learning library.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Offlinerl-kit: An elegant pytorch offline reinforcement learning library

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:14.370970Z

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Observation acc20467-b73a-4e61-aafb-0e89c97011d2 · outbound

This paper cites Hierarchically decoupled imitation for morpho- logical transfer.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Hierarchically decoupled imitation for morpho- logical transfer

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:14.363018Z

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Observation b8b6faf0-90c2-4efb-a873-f36946864a93 · outbound

This paper cites PEARL: Zero-shot cross-task preference alignment and robust reward learning for robotic manipulation.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning PEARL: Zero-shot cross-task preference alignment and robust reward learning for robotic manipulation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:14.354877Z

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Observation faa778ae-004a-4dde-8ba4-0f0d7a677f57 · outbound

This paper cites Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions

Reference 52

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This paper cites Learning robust perceptive locomotion for quadrupedal robots in the wild.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning Learning robust perceptive locomotion for quadrupedal robots in the wild

Reference 53

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This paper cites dm_control: Software and tasks for continuous control.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning dm_control: Software and tasks for continuous control

Reference 54

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This paper cites URLB: Unsupervised reinforcement learning benchmark.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning URLB: Unsupervised reinforcement learning benchmark

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:14.346513Z

Source-reported events for the cited work

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Observation 103bccc0-2245-448a-b121-76970064a4ac · outbound

This paper cites Continuous control with deep reinforcement learning.

Reference 56

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Source-reported events for the cited work

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Observation ce58efbb-6332-4e03-81a4-6bd802df36c9 · outbound

This paper cites URL https://docs.pytorch.org/ docs/stable/generated/torch.nn.TransformerEncoder.html.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning URL https://docs.pytorch.org/ docs/stable/generated/torch.nn.TransformerEncoder.html

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:14.338969Z

Source-reported events for the cited work

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Observation 9c3c1e64-16a1-4f96-92c9-bfcd397821ca · outbound

This paper cites URL https://www.gymlibrary.dev/environments/ mujoco/hopper/.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning URL https://www.gymlibrary.dev/environments/ mujoco/hopper/

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:14.331172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 26b71533-3a9e-45e8-96ae-1e3ad80676d8 · outbound

This paper cites URL https://www.gymlibrary.dev/environments/ mujoco/walker2d/.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning URL https://www.gymlibrary.dev/environments/ mujoco/walker2d/

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:14.323213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ab7ec7ee-36e9-419b-9380-ac83076703d1 · outbound

This paper cites For the Franka Kitchen tasks, we use the preference datasets from An et al.

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning For the Franka Kitchen tasks, we use the preference datasets from An et al

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:14.314920Z

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Observation 41a941b2-d2f4-404f-8b60-2b89f0b89425 · outbound

This paper cites ISSN: 2640-3498.

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:53:14.588090Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0347b73b-0a39-4a05-b278-31a057847404 · outbound

This paper cites an unresolved cited work.

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:53:14.566095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers