:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Kausik, Chinmaya, Mutti, Mirco, Pacchiano, Aldo, Tewari, Ambuj
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2402.03282
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Leveraging Offline Data in Linear Latent Contextual Bandits
by: Kausik, Chinmaya, et al.
Published: (2024)

The Context Gathering Decision Process: A POMDP Framework for Agentic Search
by: Kausik, Chinmaya, et al.
Published: (2026)

A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
by: Hong, Kihyuk, et al.
Published: (2025)

Adaptive Exploration for Multi-Reward Multi-Policy Evaluation
by: Russo, Alessio, et al.
Published: (2025)

Active Preference Optimization for Sample Efficient RLHF
by: Das, Nirjhar, et al.
Published: (2024)

Second Order Bounds for Contextual Bandits with Function Approximation
by: Pacchiano, Aldo
Published: (2024)

Reward Model Overoptimisation in Iterated RLHF
by: Wolf, Lorenz, et al.
Published: (2025)

State-free Reinforcement Learning
by: Chen, Mingyu, et al.
Published: (2024)

Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary Objectives
by: Afshar, Aida, et al.
Published: (2024)

Compute Aligned Training: Optimizing for Test Time Inference
by: Ousherovitch, Adam, et al.
Published: (2026)

Improved Training Mechanism for Reinforcement Learning via Online Model Selection
by: Afshar, Aida, et al.
Published: (2025)

ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization
by: Zhang, Chen Bo Calvin, et al.
Published: (2024)

How to Explore with Belief: State Entropy Maximization in POMDPs
by: Zamboni, Riccardo, et al.
Published: (2024)

Principled Fine-tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Reward
by: Misra, Dipendra, et al.
Published: (2026)

Towards Principled Unsupervised Multi-Agent Reinforcement Learning
by: Zamboni, Riccardo, et al.
Published: (2025)

Information-Theoretic Reward Decomposition for Generalizable RLHF
by: Mao, Liyuan, et al.
Published: (2025)

Data-Driven Online Model Selection With Regret Guarantees
by: Pacchiano, Aldo, et al.
Published: (2023)

In-Context Learning for Pure Exploration
by: Russo, Alessio, et al.
Published: (2025)

InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling
by: Miao, Yuchun, et al.
Published: (2024)

Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching
by: Fraschini, Andrea, et al.
Published: (2026)

Multiple-policy Evaluation via Density Estimation
by: Chen, Yilei, et al.
Published: (2024)

Experiment Planning with Function Approximation
by: Pacchiano, Aldo, et al.
Published: (2024)

From Parameters to Behaviors: Unsupervised Compression of the Policy Space
by: Tenedini, Davide, et al.
Published: (2025)

Provable Interactive Learning with Hindsight Instruction Feedback
by: Misra, Dipendra, et al.
Published: (2024)

The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification
by: Baharav, Tavor Z., et al.
Published: (2025)

A Characterization of List Language Identification in the Limit
by: Charikar, Moses, et al.
Published: (2025)

RLHF Workflow: From Reward Modeling to Online RLHF
by: Dong, Hanze, et al.
Published: (2024)

In-Context Learning for Pure Exploration in Continuous Spaces
by: Russo, Alessio, et al.
Published: (2026)

Circuit-Aware Reward Training: A Mechanistic Framework for Longtail Robustness in RLHF
by: Liu, Jing
Published: (2025)

Reward Compatibility: A Framework for Inverse RL
by: Lazzati, Filippo, et al.
Published: (2025)

Reward Shaping to Mitigate Reward Hacking in RLHF
by: Fu, Jiayi, et al.
Published: (2025)

Reward-Robust RLHF in LLMs
by: Yan, Yuzi, et al.
Published: (2024)

CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks
by: Wang, Hao, et al.
Published: (2026)

A Training-free Method for LLM Text Attribution
by: Radvand, Tara, et al.
Published: (2025)

How to Evaluate Reward Models for RLHF
by: Frick, Evan, et al.
Published: (2024)

Policy Filtration for RLHF to Mitigate Noise in Reward Models
by: Zhang, Chuheng, et al.
Published: (2024)

Information-Theoretic State Variable Selection for Reinforcement Learning
by: Westphal, Charles, et al.
Published: (2024)

Generalisation of RLHF under Reward Shift and Clipped KL Regularisation
by: Tang, Kenton, et al.
Published: (2026)

Bias Fitting to Mitigate Length Bias of Reward Model in RLHF
by: Zhao, Kangwen, et al.
Published: (2025)

Learning to Partially Defer for Sequences
by: Rayan, Sahana, et al.
Published: (2025)