Cover Image

AI Alignment with Changing and Influenceable Reward Functions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Carroll, Micah, Foote, Davis, Siththaranjan, Anand, Russell, Stuart, Dragan, Anca
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

When Your AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback
by: Lang, Leon, et al.
Published: (2024)

A Generalized Acquisition Function for Preference-based Reward Learning
by: Ellis, Evan, et al.
Published: (2024)

The Effective Horizon Explains Deep RL Performance in Stochastic Environments
by: Laidlaw, Cassidy, et al.
Published: (2023)

On Targeted Manipulation and Deception when Optimizing LLMs for User Feedback
by: Williams, Marcus, et al.
Published: (2024)

Correlated Proxies: A New Definition and Improved Mitigation for Reward Hacking
by: Laidlaw, Cassidy, et al.
Published: (2024)

Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF
by: Siththaranjan, Anand, et al.
Published: (2023)

The Ends Justify the Thoughts: RL-Induced Motivated Reasoning in LLM CoTs
by: Howe, Nikolaus, et al.
Published: (2025)

AssistanceZero: Scalably Solving Assistance Games
by: Laidlaw, Cassidy, et al.
Published: (2025)

BAMDP Shaping: a Unified Framework for Intrinsic Motivation and Reward Shaping
by: Lidayan, Aly, et al.
Published: (2024)

Learning to Assist Humans without Inferring Rewards
by: Myers, Vivek, et al.
Published: (2024)

Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning
by: Hong, Joey, et al.
Published: (2024)

Adversaries Can Misuse Combinations of Safe Models
by: Jones, Erik, et al.
Published: (2024)

CTRL-Rec: Controlling Recommender Systems With Natural Language
by: Carroll, Micah, et al.
Published: (2025)

Neurodivergent Influenceability as a Contingent Solution to the AI Alignment Problem
by: Hernández-Espinosa, Alberto, et al.
Published: (2025)

Bridging RL Theory and Practice with the Effective Horizon
by: Laidlaw, Cassidy, et al.
Published: (2023)

Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making
by: Myers, Vivek, et al.
Published: (2024)

Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations
by: Hong, Joey, et al.
Published: (2024)

Training LLM Agents to Empower Humans
by: Ellis, Evan, et al.
Published: (2025)

Aligning Robot and Human Representations
by: Bobu, Andreea, et al.
Published: (2023)

Adapting the Behavior of Reinforcement Learning Agents to Changing Action Spaces and Reward Functions
by: de la Rosa, Raul, et al.
Published: (2026)

Pairwise Calibrated Rewards for Pluralistic Alignment
by: Halpern, Daniel, et al.
Published: (2025)

Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment
by: Wang, Chaoqi, et al.
Published: (2025)

Cooperative Inverse Reinforcement Learning
by: Hadfield-Menell, Dylan, et al.
Published: (2016)

The Reward Model Selection Crisis in Personalized Alignment
by: Rezk, Fady, et al.
Published: (2025)

Video Diffusion Alignment via Reward Gradients
by: Prabhudesai, Mihir, et al.
Published: (2024)

Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation
by: Pan, Michelle, et al.
Published: (2024)

Towards Improving Reward Design in RL: A Reward Alignment Metric for RL Practitioners
by: Muslimani, Calarina, et al.
Published: (2025)

Repairing Reward Functions with Feedback to Mitigate Reward Hacking
by: Hatgis-Kessell, Stephane, et al.
Published: (2025)

Avoiding Catastrophe in Online Learning by Asking for Help
by: Plaut, Benjamin, et al.
Published: (2024)

Expectation Alignment: Handling Reward Misspecification in the Presence of Expectation Mismatch
by: Mechergui, Malek, et al.
Published: (2024)

Reward-Augmented Data Enhances Direct Preference Alignment of LLMs
by: Zhang, Shenao, et al.
Published: (2024)

From Demonstrations to Rewards: Alignment Without Explicit Human Preferences
by: Zeng, Siliang, et al.
Published: (2025)

Beyond Preferences in AI Alignment
by: Zhi-Xuan, Tan, et al.
Published: (2024)

Quasimetric Value Functions with Dense Rewards
by: Valieva, Khadichabonu, et al.
Published: (2024)

What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning?
by: Shihab, Ibne Farabi, et al.
Published: (2025)

Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning
by: Rajaram, Sara, et al.
Published: (2025)

Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps
by: Holderrieth, Peter, et al.
Published: (2026)

Reward Shaping for Inference-Time Alignment: A Stackelberg Game Perspective
by: Wang, Haichuan, et al.
Published: (2026)

ARGS: Alignment as Reward-Guided Search
by: Khanov, Maxim, et al.
Published: (2024)

Reward-free Alignment for Conflicting Objectives
by: Chen, Peter, et al.
Published: (2026)