Paper Citation Record · LEDGER

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations

As of 23 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 2 inbound Pith citation observations for arXiv:2505.06079.

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

pith.paper-citation-record.v1
2505.06079 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:52:24.226578Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

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

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:33:02.820968Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T02:25:55.886294Z

Reference resolution

55 of 55 outbound references displayed

verified exact1
verified fuzzy39
unresolved15
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a2e85a03-dd5b-4903-99a0-9c65b6b6053f · outbound

This paper cites Pebble: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Pebble: Feedback-efficient interactive reinforcement learning via relabeling experience and unsupervised pre-training,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:27.356075Z

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 e0148500-b221-443c-9ac9-83b1ad8eaf3f · outbound

This paper cites Surf: Semi-supervised reward learning with data augmentation for feedback-efficient preference-based reinforcement learning,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Surf: Semi-supervised reward learning with data augmentation for feedback-efficient preference-based reinforcement learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:27.331141Z

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 347a37e1-a656-476b-bf35-ca5ddc5569fd · outbound

This paper cites Active preference-based learning of reward functions,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Active preference-based learning of reward functions,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:27.306467Z

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 d5f7e1f5-b275-4787-ae92-5636312e01f9 · outbound

This paper cites Wayex: Waypoint exploration using a single demonstration,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Wayex: Waypoint exploration using a single demonstration,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:27.268190Z

Source-reported events for the cited work

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Observation bf61eb4a-90a0-4d9f-98ce-2673d3b3e562 · outbound

This paper cites Online human training of a myoelec- tric prosthesis controller via actor-critic reinforcement learn- ing,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Online human training of a myoelec- tric prosthesis controller via actor-critic reinforcement learn- ing,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:27.212477Z

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 d8f80d31-5c8f-4286-9e69-0b591ec015d7 · outbound

This paper cites A bayesian approach for policy learning from trajectory preference queries,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations A bayesian approach for policy learning from trajectory preference queries,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:27.178340Z

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 e4f19d52-326b-4655-b7c2-8bb5130b9fac · outbound

This paper cites Model-free preference-based reinforcement learning,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Model-free preference-based reinforcement learning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:27.135395Z

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 cb0a0399-3f18-42a0-bdf4-76f5bfcbbff7 · outbound

This paper cites RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback

Reference 8

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Source-reported events for the cited work

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Observation 425e39b7-0f1b-437d-87a1-b2bf58bb4687 · outbound

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

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations B-pref: Benchmarking preference-based reinforcement learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:27.087427Z

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 57d4e03e-e1e6-43bc-ba8e-64325dcbdd8c · outbound

This paper cites Rime: Robust preference-based reinforcement learning with noisy preferences,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Rime: Robust preference-based reinforcement learning with noisy preferences,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:27.036722Z

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 9df6eba4-ca71-4096-a591-f4f0e531ab76 · outbound

This paper cites Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Reference 11

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Source-reported events for the cited work

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Observation bcd98498-658b-4815-8a3a-ce7582b22bd4 · outbound

This paper cites Deep reinforcement learning from human pref- erences,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Deep reinforcement learning from human pref- erences,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.987163Z

Source-reported events for the cited work

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Observation d06e3473-61aa-433c-b696-6440b961915a · outbound

This paper cites Reward learning from human preferences and demonstrations in atari,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Reward learning from human preferences and demonstrations in atari,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.956679Z

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 454dfea3-18a0-47fb-b593-bdaaea830df0 · outbound

This paper cites Batch active preference-based learn- ing of reward functions,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Batch active preference-based learn- ing of reward functions,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.927089Z

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 3369010c-f1da-4ca0-a102-b717adf3fbd0 · outbound

This paper cites Active Preference-Based Gaussian Process Regression for Reward Learning.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Active Preference-Based Gaussian Process Regression for Reward Learning

Reference 15

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Source-reported events for the cited work

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Observation a9b516d6-919d-424e-8a61-dd977c1e1cb9 · outbound

This paper cites Reward uncertainty for exploration in preference-based reinforcement learning,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Reward uncertainty for exploration in preference-based reinforcement learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.846709Z

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 17db3971-04d1-4ce7-8fea-79c9fe144022 · outbound

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

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Learning from noisy labels with deep neural networks: A survey,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.756271Z

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 d7b30a1e-8da2-4bbb-9df4-941b35ff97c5 · outbound

This paper cites Denoising implicit feedback for recommendation,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Denoising implicit feedback for recommendation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.620633Z

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 94d01dd7-6ee4-4b61-adc5-73f494a19a1e · outbound

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

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Co-teaching: Robust training of deep neural networks with extremely noisy labels,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.548642Z

Source-reported events for the cited work

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Observation 659a0e5e-01df-4e28-8da3-f142664a56e7 · outbound

This paper cites Training deep neural- networks using a noise adaptation layer,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Training deep neural- networks using a noise adaptation layer,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.433851Z

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 bf61dbfc-642d-4fdc-8098-093dbc1a813b · outbound

This paper cites Does label smoothing mitigate label noise?.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Does label smoothing mitigate label noise?

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.398056Z

Source-reported events for the cited work

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Observation 809c688c-896b-4e6a-8f51-dfc29ab12a7b · outbound

This paper cites Reinforcement Learning from Diverse Human Preferences.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Reinforcement Learning from Diverse Human Preferences

Reference 22

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Observation 94d72d0d-ba29-4fb6-8d76-85939b25c775 · outbound

This paper cites TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies

Reference 23

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Source-reported events for the cited work

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Observation f35022f0-c43f-4fb3-8710-8f8f839e699e · outbound

This paper cites What is point supervision worth in video instance segmentation?.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations What is point supervision worth in video instance segmentation?

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.377251Z

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 e445d69a-2941-40cc-b4e9-9e1f01268b61 · outbound

This paper cites Uvis: Unsupervised video instance segmen- tation,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Uvis: Unsupervised video instance segmen- tation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.350294Z

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 c825d4c1-175b-4a08-b48a-477946a698da · outbound

This paper cites LMPriors: Pre-Trained Language Models as Task-Specific Priors.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations LMPriors: Pre-Trained Language Models as Task-Specific Priors

Reference 26

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Source-reported events for the cited work

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Observation d42d39ea-908a-4dc0-9bd3-e1475742e268 · outbound

This paper cites Guiding pretraining in reinforce- ment learning with large language models,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Guiding pretraining in reinforce- ment learning with large language models,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.308706Z

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 6732c782-95b1-4d40-87b0-5f456182d21d · outbound

This paper cites Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning

Reference 28

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Observation 4f78414c-e0db-4f4e-9249-de8d935ecf70 · outbound

This paper cites Structured attentions for visual question answering,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Structured attentions for visual question answering,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.248067Z

Source-reported events for the cited work

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Observation 16abd0f0-a1cc-45f7-9da4-832a4dc15240 · outbound

This paper cites Learning semantic correspondence with sparse an- notations,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Learning semantic correspondence with sparse an- notations,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.152235Z

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 605291f7-9742-4acf-ae0b-745152967fb8 · outbound

This paper cites Confidence-aware adversarial learning for self-supervised semantic matching,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Confidence-aware adversarial learning for self-supervised semantic matching,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.080627Z

Source-reported events for the cited work

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Observation 755a868c-18a9-4cab-b531-912632d5778f · outbound

This paper cites Motif: Intrinsic motivation from artificial intelligence feedback,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Motif: Intrinsic motivation from artificial intelligence feedback,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:26.001012Z

Source-reported events for the cited work

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Observation 3d5c9583-b766-4635-88ae-8745f8e5e585 · outbound

This paper cites Towards scalable neural representation for diverse videos,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Towards scalable neural representation for diverse videos,

Reference 33

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Source-reported events for the cited work

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Observation a9f2a9e1-6d3c-4bd7-a1a9-099d2fa47a32 · outbound

This paper cites Dynamic context correspondence network for semantic alignment,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Dynamic context correspondence network for semantic alignment,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:25.869633Z

Source-reported events for the cited work

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Observation e0c4e6b8-b6d7-433b-b37e-b3560a0073ee · outbound

This paper cites ARDuP: Active Region Video Diffusion for Universal Policies.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations ARDuP: Active Region Video Diffusion for Universal Policies

Reference 35

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This paper cites P3-po: Prescriptive point priors for visuo-spatial generalization of robot policies,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations P3-po: Prescriptive point priors for visuo-spatial generalization of robot policies,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:25.804858Z

Source-reported events for the cited work

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Observation 1f38efc1-8e5f-40a4-9542-e481cabe3278 · outbound

This paper cites AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T22:52:24.036110Z

Source-reported events for the cited work

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Observation f4347a6e-3b8c-48cc-99ab-08d9de61f246 · outbound

This paper cites Is Imitation All You Need? Generalized Decision-Making with Dual-Phase Training.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Is Imitation All You Need? Generalized Decision-Making with Dual-Phase Training

Reference 38

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Source-reported events for the cited work

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Observation 291a3c4f-2bab-4cd5-917b-73a32052f909 · outbound

This paper cites PRISE: LLM-style sequence compression for learning temporal action abstractions in control,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations PRISE: LLM-style sequence compression for learning temporal action abstractions in control,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:25.780442Z

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 30359195-88b4-414e-8c2a-b27de4879bc7 · outbound

This paper cites Premier-taco is a few-shot policy learner: Pretraining mul- titask representation via temporal action-driven contrastive loss,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Premier-taco is a few-shot policy learner: Pretraining mul- titask representation via temporal action-driven contrastive loss,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:25.692940Z

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 eaa4af82-dfed-4b83-a9c5-36fdb8271e13 · outbound

This paper cites TACO: Temporal latent action- driven contrastive loss for visual reinforcement learning,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations TACO: Temporal latent action- driven contrastive loss for visual reinforcement learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:25.655812Z

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 f084a526-0144-4825-bd6c-5133a1132641 · outbound

This paper cites Text2Reward: Reward Shaping with Language Models for Reinforcement Learning.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Text2Reward: Reward Shaping with Language Models for Reinforcement Learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T22:52:24.105867Z

Source-reported events for the cited work

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Observation 58299a99-58f8-457c-b70b-e78e651891c2 · outbound

This paper cites Reward Design with Language Models.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Reward Design with Language Models

Reference 43

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Source-reported events for the cited work

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Observation bf60b1ca-c877-4d7c-a23c-5396d3d49dbd · outbound

This paper cites Reinforcement learning: An introduction,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Reinforcement learning: An introduction,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:25.622118Z

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 7e3206d1-26b7-4905-804f-7aa75a600cb2 · outbound

This paper cites Meta-reward-net: Implicitly differentiable reward learning for preference-based reinforcement learning,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Meta-reward-net: Implicitly differentiable reward learning for preference-based reinforcement learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:25.586756Z

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 22b1ecfc-84e9-48c5-b1a7-520499d955fe · outbound

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

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Preference Transformer: Modeling Human Preferences using Transformers for RL

Reference 46

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Source-reported events for the cited work

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Observation d3d2fc51-f8c5-4372-a4fd-435e3661a722 · outbound

This paper cites Rank analysis of incom- plete block designs: I. the method of paired comparisons,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Rank analysis of incom- plete block designs: I. the method of paired comparisons,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:25.543883Z

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 2592a74d-851e-46be-83ba-5d3bd231ff49 · outbound

This paper cites Three-teaching: A three-way decision framework to handle noisy labels,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Three-teaching: A three-way decision framework to handle noisy labels,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:25.476004Z

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 8a917d95-51e0-4e03-b7e6-0bde1080d9d9 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 49

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Observation 1e65c652-ed01-4230-93aa-2bc5d7e87d61 · outbound

This paper cites Nearest neighbor estimates of entropy,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Nearest neighbor estimates of entropy,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:25.422313Z

Source-reported events for the cited work

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Observation 5b66dbde-c3fe-490b-9fa8-e377e24a9b87 · outbound

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

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Soft actor- critic: Off-policy maximum entropy deep reinforcement learn- ing with a stochastic actor,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:25.376078Z

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Observation 05e69f5a-eb38-4101-b8fc-c2d38e73e0c8 · outbound

This paper cites Gemini: A family of highly capable multimodal models,.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Gemini: A family of highly capable multimodal models,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:25.338214Z

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

Observation 2ca1742f-2fdb-480e-83e3-c40e37230947 · outbound

This paper cites Reward Uncertainty for Exploration in Preference-based Reinforcement Learning.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Reward Uncertainty for Exploration in Preference-based Reinforcement Learning

Reference 2022

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Observation d09e3dd3-6de9-48af-818e-5448436fe3e6 · outbound

This paper cites Motif: Intrinsic Motivation from Artificial Intelligence Feedback.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Motif: Intrinsic Motivation from Artificial Intelligence Feedback

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T22:52:23.991919Z

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Observation 5ee37f6f-7f98-430f-8e2a-9b515e862e19 · outbound

This paper cites Available: https://openreview.net/forum?id= p225Od0aYt.

TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations Available: https://openreview.net/forum?id= p225Od0aYt

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:52:25.729269Z

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Pith citing papers

Observation b34a91bc-ede1-4ca6-9fce-25bd053e69b7 · inbound

PB$^2$: Preference Space Exploration via Population-Based Methods in Preference-Based Reinforcement Learning cites this paper.

PB$^2$: Preference Space Exploration via Population-Based Methods in Preference-Based Reinforcement Learning TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrations

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T00:33:02.820968Z

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

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF cites this paper.

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

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T02:25:55.887521Z

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