Provable Reward-Agnostic Preference-Based Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Zhan, Wenhao, Uehara, Masatoshi, Sun, Wen, Lee, Jason D. |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond
by: Chen, Fan, et al.
Published: (2022)
by: Chen, Fan, et al.
Published: (2022)
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
by: Lu, Miao, et al.
Published: (2022)
by: Lu, Miao, et al.
Published: (2022)
Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining
by: Lin, Licong, et al.
Published: (2023)
by: Lin, Licong, et al.
Published: (2023)
Learning Hierarchical Polynomials of Multiple Nonlinear Features with Three-Layer Networks
by: Fu, Hengyu, et al.
Published: (2024)
by: Fu, Hengyu, et al.
Published: (2024)
On the Provable Performance Guarantee of Efficient Reasoning Models
by: Zeng, Hao, et al.
Published: (2025)
by: Zeng, Hao, et al.
Published: (2025)
Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits
by: Chen, Fan, et al.
Published: (2025)
by: Chen, Fan, et al.
Published: (2025)
Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization
by: Liu, Shuang, et al.
Published: (2025)
by: Liu, Shuang, et al.
Published: (2025)
Neural Networks Learn Generic Multi-Index Models Near Information-Theoretic Limit
by: Zhang, Bohan, et al.
Published: (2025)
by: Zhang, Bohan, et al.
Published: (2025)
Characteristic Learning for Provable One Step Generation
by: Ding, Zhao, et al.
Published: (2024)
by: Ding, Zhao, et al.
Published: (2024)
Labels or Preferences? Budget-Constrained Learning with Human Judgments over AI-Generated Outputs
by: Dong, Zihan, et al.
Published: (2026)
by: Dong, Zihan, et al.
Published: (2026)
Scaling Laws in Linear Regression: Compute, Parameters, and Data
by: Lin, Licong, et al.
Published: (2024)
by: Lin, Licong, et al.
Published: (2024)
Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning
by: Li, Gen, et al.
Published: (2023)
by: Li, Gen, et al.
Published: (2023)
Beyond identifiability: Learning causal representations with few environments and finite samples
by: Lee, Inbeom, et al.
Published: (2026)
by: Lee, Inbeom, et al.
Published: (2026)
A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment
by: Yu, Hao
Published: (2026)
by: Yu, Hao
Published: (2026)
Sail into the Headwind: Alignment via Robust Rewards and Dynamic Labels against Reward Hacking
by: Rashidinejad, Paria, et al.
Published: (2024)
by: Rashidinejad, Paria, et al.
Published: (2024)
Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration
by: Foster, Dylan J., et al.
Published: (2025)
by: Foster, Dylan J., et al.
Published: (2025)
Statistical and Algorithmic Foundations of Reinforcement Learning
by: Chi, Yuejie, et al.
Published: (2025)
by: Chi, Yuejie, et al.
Published: (2025)
A Differential and Pointwise Control Approach to Reinforcement Learning
by: Nguyen, Minh, et al.
Published: (2024)
by: Nguyen, Minh, et al.
Published: (2024)
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy
by: Chen, Fan, et al.
Published: (2025)
by: Chen, Fan, et al.
Published: (2025)
Reinforcement Learning for Microcanonical Graph Ensemble with Assortativity Constraints
by: Choi, Hoyun, et al.
Published: (2026)
by: Choi, Hoyun, et al.
Published: (2026)
Decoupled Continuous-Time Reinforcement Learning via Hamiltonian Flow
by: Nguyen, Minh
Published: (2026)
by: Nguyen, Minh
Published: (2026)
Towards Efficient Online Exploration for Reinforcement Learning with Human Feedback
by: Li, Gen, et al.
Published: (2025)
by: Li, Gen, et al.
Published: (2025)
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
by: Rajendran, Goutham, et al.
Published: (2024)
by: Rajendran, Goutham, et al.
Published: (2024)
Compression, Generalization and Learning
by: Campi, Marco C., et al.
Published: (2023)
by: Campi, Marco C., et al.
Published: (2023)
FraPPE: Fast and Efficient Preference-based Pure Exploration
by: Das, Udvas, et al.
Published: (2025)
by: Das, Udvas, et al.
Published: (2025)
Online Learning with Unknown Constraints
by: Sridharan, Karthik, et al.
Published: (2024)
by: Sridharan, Karthik, et al.
Published: (2024)
Adaptive Sample Aggregation In Transfer Learning
by: Hanneke, Steve, et al.
Published: (2024)
by: Hanneke, Steve, et al.
Published: (2024)
Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review
by: Uehara, Masatoshi, et al.
Published: (2024)
by: Uehara, Masatoshi, et al.
Published: (2024)
Conformal Prediction for Privacy-Preserving Machine Learning
by: Balinsky, Alexander David, et al.
Published: (2025)
by: Balinsky, Alexander David, et al.
Published: (2025)
Learning with Differentially Private (Sliced) Wasserstein Gradients
by: Rodríguez-Vítores, David, et al.
Published: (2025)
by: Rodríguez-Vítores, David, et al.
Published: (2025)
Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Ability
by: Yu, Lijia, et al.
Published: (2025)
by: Yu, Lijia, et al.
Published: (2025)
Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models
by: Li, Gen, et al.
Published: (2024)
by: Li, Gen, et al.
Published: (2024)
Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
by: Shen, Zhaiming, et al.
Published: (2025)
by: Shen, Zhaiming, et al.
Published: (2025)
Chemical Reaction Networks Learn Better than Spiking Neural Networks
by: Jaffard, Sophie, et al.
Published: (2026)
by: Jaffard, Sophie, et al.
Published: (2026)
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
by: Foster, Dylan J., et al.
Published: (2024)
by: Foster, Dylan J., et al.
Published: (2024)
A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
by: Hazard, Christopher J., et al.
Published: (2025)
by: Hazard, Christopher J., et al.
Published: (2025)
A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)
by: Chakraborty, Saptarshi, et al.
Published: (2024)
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
by: Javanmard, Adel, et al.
Published: (2024)
by: Javanmard, Adel, et al.
Published: (2024)
Towards a Sharp Analysis of Offline Policy Learning for $f$-Divergence-Regularized Contextual Bandits
by: Zhao, Qingyue, et al.
Published: (2025)
by: Zhao, Qingyue, et al.
Published: (2025)
When Can We Reuse a Calibration Set for Multiple Conformal Predictions?
by: Balinsky, A. A., et al.
Published: (2025)
by: Balinsky, A. A., et al.
Published: (2025)
Similar Items
-
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond
by: Chen, Fan, et al.
Published: (2022) -
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
by: Lu, Miao, et al.
Published: (2022) -
Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining
by: Lin, Licong, et al.
Published: (2023) -
Learning Hierarchical Polynomials of Multiple Nonlinear Features with Three-Layer Networks
by: Fu, Hengyu, et al.
Published: (2024) -
On the Provable Performance Guarantee of Efficient Reasoning Models
by: Zeng, Hao, et al.
Published: (2025)