Cover Image

Learning Pareto-Optimal Rewards from Noisy Preferences: A Framework for Multi-Objective Inverse Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cherukuri, Kalyan, Lala, Aarav
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Computational Geometry
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning
by: Lala, Aarav, et al.
Published: (2025)

Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning
by: Cherukuri, Kalyan, et al.
Published: (2025)

Low-Rank Matrix Approximation for Neural Network Compression
by: Cherukuri, Kalyan, et al.
Published: (2025)

Pareto-Optimal Learning from Preferences with Hidden Context
by: Bahlous-Boldi, Ryan, et al.
Published: (2024)

Optimal Transport for LLM Reward Modeling from Noisy Preference
by: Pan, Licheng, et al.
Published: (2026)

Hindsight Preference Replay Improves Preference-Conditioned Multi-Objective Reinforcement Learning
by: Shianifar, Jonaid, et al.
Published: (2026)

Inverse Reinforcement Learning with Dynamic Reward Scaling for LLM Alignment
by: Cheng, Ruoxi, et al.
Published: (2025)

Neural Lyapunov Function Approximation with Self-Supervised Reinforcement Learning
by: McCutcheon, Luc, et al.
Published: (2025)

Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards
by: Wang, Haoxiang, et al.
Published: (2024)

Pareto Multi-Objective Alignment for Language Models
by: He, Qiang, et al.
Published: (2025)

Rate or Fate? RLV$^\varepsilon$R: Reinforcement Learning with Verifiable Noisy Rewards
by: Rad, Ali, et al.
Published: (2026)

Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach
by: Kim, Kihyun, et al.
Published: (2026)

PA2D-MORL: Pareto Ascent Directional Decomposition based Multi-Objective Reinforcement Learning
by: Hu, Tianmeng, et al.
Published: (2026)

RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences
by: Cheng, Jie, et al.
Published: (2024)

Reinforcement Learning with LTL and $ω$-Regular Objectives via Optimality-Preserving Translation to Average Rewards
by: Le, Xuan-Bach, et al.
Published: (2024)

Learning Pareto Set for Multi-Objective Continuous Robot Control
by: Shu, Tianye, et al.
Published: (2024)

Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning
by: Wei, Ziquan, et al.
Published: (2024)

EMP: Effective Multidimensional Persistence for Graph Representation Learning
by: Segovia-Dominguez, Ignacio, et al.
Published: (2024)

Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers
by: Cai, Xin-Qiang, et al.
Published: (2025)

POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator
by: Lomurno, Eugenio, et al.
Published: (2024)

Topology-Preserving Neural Operator Learning via Hodge Decomposition
by: Zheng, Dongzhe, et al.
Published: (2026)

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization
by: Ambadkar, Tanmay, et al.
Published: (2026)

An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning
by: Lin, Qian, et al.
Published: (2024)

Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning
by: Gu, Shangding, et al.
Published: (2024)

Preference Poisoning Attacks on Reward Model Learning
by: Wu, Junlin, et al.
Published: (2024)

From Theory to Throughput: CUDA-Optimized APML for Large-Batch 3D Learning
by: Sharifipour, Sasan, et al.
Published: (2025)

Reinforcement learning for automatic quadrilateral mesh generation: a soft actor-critic approach
by: Pan, Jie, et al.
Published: (2022)

Property-driven Protein Inverse Folding With Multi-Objective Preference Alignment
by: Hou, Xiaoyang, et al.
Published: (2026)

Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection
by: Neupane, Dhiraj, et al.
Published: (2026)

Is Optimal Transport Necessary for Inverse Reinforcement Learning?
by: Dong, Zixuan, et al.
Published: (2025)

Listwise Reward Estimation for Offline Preference-based Reinforcement Learning
by: Choi, Heewoong, et al.
Published: (2024)

Adaptive Alignment: Dynamic Preference Adjustments via Multi-Objective Reinforcement Learning for Pluralistic AI
by: Harland, Hadassah, et al.
Published: (2024)

MODULI: Unlocking Preference Generalization via Diffusion Models for Offline Multi-Objective Reinforcement Learning
by: Yuan, Yifu, et al.
Published: (2024)

Residual Reward Models for Preference-based Reinforcement Learning
by: Cao, Chenyang, et al.
Published: (2025)

Hindsight PRIORs for Reward Learning from Human Preferences
by: Verma, Mudit, et al.
Published: (2024)

Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning
by: Schlaginhaufen, Andreas, et al.
Published: (2024)

TW-CRL: Time-Weighted Contrastive Reward Learning for Efficient Inverse Reinforcement Learning
by: Li, Yuxuan, et al.
Published: (2025)

Learning Robust Reward Machines from Noisy Labels
by: Parac, Roko, et al.
Published: (2024)

Stabilizing Long-term Multi-turn Reinforcement Learning with Gated Rewards
by: Sun, Zetian, et al.
Published: (2025)

Reinforcement Learning with Conditional Expectation Reward
by: Xiao, Changyi, et al.
Published: (2026)