Thompson Sampling in Online RLHF with General Function Approximation | Paper Citation Record

Observation e4249897-9122-4b45-8413-d0da2bfbad51 · outbound

This paper cites Analysis of thompson sampling for the multi-armed bandit problem.

Thompson Sampling in Online RLHF with General Function Approximation Analysis of thompson sampling for the multi-armed bandit problem

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:57.353799Z

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Observation 20d9d1ba-99c3-4248-8eb1-27b873063592 · outbound

This paper cites Thompson sampling for contextual bandits with linear payoffs.

Thompson Sampling in Online RLHF with General Function Approximation Thompson sampling for contextual bandits with linear payoffs

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:57.169067Z

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Observation 123a3047-6d7c-4587-b00f-bc2f1b16ed18 · outbound

This paper cites Near-optimal regret bounds for thompson sampling.J.

Thompson Sampling in Online RLHF with General Function Approximation Near-optimal regret bounds for thompson sampling.J

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:57.050843Z

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Observation ffa5cb5e-80ae-48f0-a949-f154c1aeaa4c · outbound

This paper cites Preference-based online learning with dueling bandits: a survey.J.

Thompson Sampling in Online RLHF with General Function Approximation Preference-based online learning with dueling bandits: a survey.J

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:56.921778Z

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Observation 068d17e4-c7b3-451c-b836-a752a0dd49bd · outbound

This paper cites an unresolved cited work.

Thompson Sampling in Online RLHF with General Function Approximation Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:43:56.839597Z

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Observation 1c0b73b2-aa5c-49b1-990e-9906ac6bb7ed · outbound

This paper cites Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

Thompson Sampling in Online RLHF with General Function Approximation Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 6

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Observation bf60b172-58a5-4b1c-a562-17d5db2b54ab · outbound

This paper cites Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation.

Thompson Sampling in Online RLHF with General Function Approximation Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:47.660403Z

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Observation df94777c-9a6c-42ee-a834-a528c133d4c5 · outbound

This paper cites On the Weaknesses of Reinforcement Learning for Neural Machine Translation.

Thompson Sampling in Online RLHF with General Function Approximation On the Weaknesses of Reinforcement Learning for Neural Machine Translation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:47.697268Z

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Observation ec97d09e-1422-4c8e-beb9-24e7242a2e90 · outbound

This paper cites Christiano, Jan Leike, Tom B.

Thompson Sampling in Online RLHF with General Function Approximation Christiano, Jan Leike, Tom B

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:56.773058Z

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Observation c3b6e898-2535-472e-bb8e-1f0b4efb17ce · outbound

This paper cites RAFT: Reward ranked finetuning for generative foundation model alignment.Transactions on Machine Learning Research, 2023.

Thompson Sampling in Online RLHF with General Function Approximation RAFT: Reward ranked finetuning for generative foundation model alignment.Transactions on Machine Learning Research, 2023

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:56.691296Z

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Observation 9b05ccd0-1758-4e94-898e-5e4cf8905e2f · outbound

This paper cites Schapire, Aleksandrs Slivkins, and Masrour Zoghi.

Thompson Sampling in Online RLHF with General Function Approximation Schapire, Aleksandrs Slivkins, and Masrour Zoghi

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:56.598480Z

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Observation 19324a54-6076-42a7-9974-d420050cd73f · outbound

This paper cites Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO.

Thompson Sampling in Online RLHF with General Function Approximation Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:47.833667Z

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Observation 3b0a6cb4-d055-45c1-963e-d178d68d1d07 · outbound

This paper cites Foster and Alexander Rakhlin.

Thompson Sampling in Online RLHF with General Function Approximation Foster and Alexander Rakhlin

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:56.515634Z

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Observation 7e17df10-3712-4376-ba25-69acb9567738 · outbound

This paper cites Scaling laws for reward model overoptimization.

Thompson Sampling in Online RLHF with General Function Approximation Scaling laws for reward model overoptimization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:56.445967Z

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Observation f93d914f-0c4c-473a-82e8-2c013645e3ae · outbound

This paper cites A General Theoretical Paradigm to Understand Learning from Human Preferences.

Thompson Sampling in Online RLHF with General Function Approximation A General Theoretical Paradigm to Understand Learning from Human Preferences

Reference 15

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Observation e83e8437-ccdd-42d5-b84d-f82d4a42da7e · outbound

This paper cites Reinforced Self-Training (ReST) for Language Modeling.

Thompson Sampling in Online RLHF with General Function Approximation Reinforced Self-Training (ReST) for Language Modeling

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:47.949387Z

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Observation 577cacf1-c0d0-4a3d-81be-5207f551a71d · outbound

This paper cites Randomized Exploration for Reinforcement Learning with General Value Function Approximation.

Thompson Sampling in Online RLHF with General Function Approximation Randomized Exploration for Reinforcement Learning with General Value Function Approximation

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:43:51.698037Z

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Observation f297a784-efaf-4ffe-89e3-7dcd0598597c · outbound

This paper cites Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo.

Thompson Sampling in Online RLHF with General Function Approximation Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:48.010070Z

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Observation c42b36f4-36c4-48c0-91b7-2a61f39d6dd5 · outbound

This paper cites Learning trajectory preferences for manipulators via iterative improvement.

Thompson Sampling in Online RLHF with General Function Approximation Learning trajectory preferences for manipulators via iterative improvement

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:56.372101Z

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Observation 8fcfabfa-f0f1-4928-b963-d64cda7e98fd · outbound

This paper cites Schapire.

Thompson Sampling in Online RLHF with General Function Approximation Schapire

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:56.305869Z

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Observation 8d7c2828-6114-47ee-9b1c-75a721be65d2 · outbound

This paper cites Bellman eluder dimension: new rich classes of rl problems, and sample-efficient algorithms.

Thompson Sampling in Online RLHF with General Function Approximation Bellman eluder dimension: new rich classes of rl problems, and sample-efficient algorithms

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:56.160284Z

Source-reported events for the cited work

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Observation 0c2c094a-4035-4108-932e-73172d5f9e74 · outbound

This paper cites An Introduction to Variational Autoencoders.

Thompson Sampling in Online RLHF with General Function Approximation An Introduction to Variational Autoencoders

Reference 22

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Observation 42031251-04a4-4d60-98f6-1d92937d10ec · outbound

This paper cites Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism.

Thompson Sampling in Online RLHF with General Function Approximation Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:48.277156Z

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Observation f0776b47-d595-408d-8199-0e20cbb831ad · outbound

This paper cites ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models.

Thompson Sampling in Online RLHF with General Function Approximation ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:48.349648Z

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Observation 5e9a7c7f-6d8b-48bb-a13c-f3dd10c93cb9 · outbound

This paper cites Statistical Rejection Sampling Improves Preference Optimization.

Thompson Sampling in Online RLHF with General Function Approximation Statistical Rejection Sampling Improves Preference Optimization

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:48.431444Z

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Observation 2d07115f-7ad9-4934-859e-b3cfd268c94f · outbound

This paper cites Roberts, Matthew E.

Thompson Sampling in Online RLHF with General Function Approximation Roberts, Matthew E

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:55.983442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 99e536a6-804c-4e8e-9d7d-6a8133fa2060 · outbound

This paper cites Dueling Posterior Sampling for Preference-Based Reinforcement Learning.

Thompson Sampling in Online RLHF with General Function Approximation Dueling Posterior Sampling for Preference-Based Reinforcement Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:48.515394Z

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Observation d2dfc404-e7b3-4272-a6ce-8e481e070dfe · outbound

This paper cites GPT-4 Technical Report.

Thompson Sampling in Online RLHF with General Function Approximation GPT-4 Technical Report

Reference 28

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Observation d119f7ce-7e96-44a5-8ae4-32de28a7a17b · outbound

This paper cites Randomized prior functions for deep reinforcement learning.

Thompson Sampling in Online RLHF with General Function Approximation Randomized prior functions for deep reinforcement learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:55.724220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 49fef49c-a677-43d7-aabc-9d0303095e41 · outbound

This paper cites Approximate thompson sampling via epistemic neural networks.

Thompson Sampling in Online RLHF with General Function Approximation Approximate thompson sampling via epistemic neural networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:55.458395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a4149ebe-e20f-44e2-98f4-d8e5ea133972 · outbound

This paper cites an unresolved cited work.

Thompson Sampling in Online RLHF with General Function Approximation Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:43:55.265065Z

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Observation 6f9f557f-4443-4c58-8032-dc79c85f99d1 · outbound

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

Thompson Sampling in Online RLHF with General Function Approximation Training language models to follow instructions with human feedback

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:48.769424Z

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Observation 861b40f8-ed0f-40de-9e17-2b78ec60c871 · outbound

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

Thompson Sampling in Online RLHF with General Function Approximation Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:48.796716Z

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Observation 211392fb-78f6-416f-88c2-2596ae83e015 · outbound

This paper cites Worst-Case Regret Bounds for Exploration via Randomized Value Functions.

Thompson Sampling in Online RLHF with General Function Approximation Worst-Case Regret Bounds for Exploration via Randomized Value Functions

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:43:51.321069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 55c69687-57ec-4809-8c77-83e041a8c371 · outbound

This paper cites Learning to optimize via posterior sampling.Math.

Thompson Sampling in Online RLHF with General Function Approximation Learning to optimize via posterior sampling.Math

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:55.197918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bc4131b1-1e3a-4dc0-9db7-ff14eb6c686a · outbound

This paper cites Optimal algorithms for stochastic contextual preference bandits.

Thompson Sampling in Online RLHF with General Function Approximation Optimal algorithms for stochastic contextual preference bandits

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:55.096880Z

Source-reported events for the cited work

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Observation d14f95a0-6435-44a0-ad5c-650065971df0 · outbound

This paper cites Efficient and optimal algorithms for contextual dueling bandits under realizability.

Thompson Sampling in Online RLHF with General Function Approximation Efficient and optimal algorithms for contextual dueling bandits under realizability

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:54.840829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 96c7ef00-ab1d-4d6b-afc8-59b1df7108b3 · outbound

This paper cites Dueling rl: Reinforcement learning with trajectory preferences.

Thompson Sampling in Online RLHF with General Function Approximation Dueling rl: Reinforcement learning with trajectory preferences

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:54.465692Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 49191887-b0d3-43ff-95e7-cb122383c61b · outbound

This paper cites Proximal Policy Optimization Algorithms.

Thompson Sampling in Online RLHF with General Function Approximation Proximal Policy Optimization Algorithms

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:48.940005Z

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Observation 279c74ef-69af-4a5a-80c5-1c6e4b99451a · outbound

This paper cites Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano.

Thompson Sampling in Online RLHF with General Function Approximation Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:54.117813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fa5d1bcb-9c9a-48b3-8d23-0f6bb19a2314 · outbound

This paper cites Thompson.

Thompson Sampling in Online RLHF with General Function Approximation Thompson

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:53.933023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d0e6e6ee-2a2d-4ea5-9d0c-675fddd8b452 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Thompson Sampling in Online RLHF with General Function Approximation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:49.018024Z

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Observation 5ade00be-bf41-4888-ae26-23ff03ca7444 · outbound

This paper cites van de Geer.

Thompson Sampling in Online RLHF with General Function Approximation van de Geer

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:53.773838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 76980b50-eb90-47f5-9a42-dc7bc440db34 · outbound

This paper cites Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints.

Thompson Sampling in Online RLHF with General Function Approximation Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:49.083529Z

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Observation 7f75d0cc-fa25-45bd-bef3-aa7b602e3ef7 · outbound

This paper cites Thompson sampling for combinatorial semi-bandits.

Thompson Sampling in Online RLHF with General Function Approximation Thompson sampling for combinatorial semi-bandits

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:53.720079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 66e2e15c-29c5-4fef-b039-035424f36808 · outbound

This paper cites Is rlhf more difficult than standard rl? a theoretical perspective.

Thompson Sampling in Online RLHF with General Function Approximation Is rlhf more difficult than standard rl? a theoretical perspective

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:53.586179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 32e371bb-2530-41ee-91aa-d4f60570624f · outbound

This paper cites A survey of preference-based reinforcement learning methods.Journal of Machine Learning Research, 18(136):1–46, 2017.

Thompson Sampling in Online RLHF with General Function Approximation A survey of preference-based reinforcement learning methods.Journal of Machine Learning Research, 18(136):1–46, 2017

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:53.466104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation aaaeabf1-1a17-4283-b3f8-afbfa8d4821b · outbound

This paper cites Making RL with Preference-based Feedback Efficient via Randomization.

Thompson Sampling in Online RLHF with General Function Approximation Making RL with Preference-based Feedback Efficient via Randomization

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:49.305402Z

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Observation 3bff1bf3-7f85-42d6-9c5b-5fa3c5a8987b · outbound

This paper cites Borda regret minimization for generalized linear dueling bandits.

Thompson Sampling in Online RLHF with General Function Approximation Borda regret minimization for generalized linear dueling bandits

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:53.309730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d633d1f9-2b01-4aa8-929c-e316d7ca4f71 · outbound

This paper cites Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint.

Thompson Sampling in Online RLHF with General Function Approximation Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:49.435213Z

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Observation 50e372fb-8206-41ca-be88-96913864589f · outbound

This paper cites Near-optimal randomized exploration for tabular markov decision processes.

Thompson Sampling in Online RLHF with General Function Approximation Near-optimal randomized exploration for tabular markov decision processes

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:53.149868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 358a7f11-bc21-4f36-ae13-e03d2f64e846 · outbound

This paper cites Yang, Aarti Singh, and Artur Dubrawski.

Thompson Sampling in Online RLHF with General Function Approximation Yang, Aarti Singh, and Artur Dubrawski

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:52.943796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2224a913-2e3b-4ce0-89b8-bb0c530ca86a · outbound

This paper cites RRHF: Rank Responses to Align Language Models with Human Feedback without tears.

Thompson Sampling in Online RLHF with General Function Approximation RRHF: Rank Responses to Align Language Models with Human Feedback without tears

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:49.730231Z

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Observation efdf6433-3dac-4f16-8619-95bc291194bb · outbound

This paper cites The k-armed dueling bandits problem.Journal of Computer and System Sciences, 78(5):1538–1556, 2012.

Thompson Sampling in Online RLHF with General Function Approximation The k-armed dueling bandits problem.Journal of Computer and System Sciences, 78(5):1538–1556, 2012

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:52.725910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 74185e95-b077-4d1a-8ed4-77e557bb356e · outbound

This paper cites Frequentist Regret Bounds for Randomized Least-Squares Value Iteration.

Thompson Sampling in Online RLHF with General Function Approximation Frequentist Regret Bounds for Randomized Least-Squares Value Iteration

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:43:50.963293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation be121500-6594-443b-9b65-15b395ac386f · outbound

This paper cites Provable Offline Preference-Based Reinforcement Learning.

Thompson Sampling in Online RLHF with General Function Approximation Provable Offline Preference-Based Reinforcement Learning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:50.030374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52573980-9b4b-4c97-91d0-417248f344a7 · outbound

This paper cites Lee, and Wen Sun.

Thompson Sampling in Online RLHF with General Function Approximation Lee, and Wen Sun

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:52.502747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0b8a432e-e368-4c56-b05f-05153a48c8e2 · outbound

This paper cites SLiC-HF: Sequence Likelihood Calibration with Human Feedback.

Thompson Sampling in Online RLHF with General Function Approximation SLiC-HF: Sequence Likelihood Calibration with Human Feedback

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:50.251391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03c377c5-e3ab-436d-8210-6ccd562be404 · outbound

This paper cites Fine-Tuning Language Models with Advantage-Induced Policy Alignment.

Thompson Sampling in Online RLHF with General Function Approximation Fine-Tuning Language Models with Advantage-Induced Policy Alignment

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:50.418201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77e137d6-8164-40ce-abb3-10a5008f955d · outbound

This paper cites Efficient active learning with abstention.

Thompson Sampling in Online RLHF with General Function Approximation Efficient active learning with abstention

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:52.301764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b14aa731-9812-46c9-a59b-4c5cdea52644 · outbound

This paper cites an unresolved cited work.

Thompson Sampling in Online RLHF with General Function Approximation Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:43:52.029084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers