Cover Image

Scalable Offline Model-Based RL with Action Chunks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Park, Kwanyoung, Park, Seohong, Lee, Youngwoon, Levine, Sergey
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Model-based Offline Reinforcement Learning with Lower Expectile Q-Learning
by: Park, Kwanyoung, et al.
Published: (2024)

Chunk-Guided Q-Learning
by: Song, Gwanwoo, et al.
Published: (2026)

HIQL: Offline Goal-Conditioned RL with Latent States as Actions
by: Park, Seohong, et al.
Published: (2023)

Decoupled Q-Chunking
by: Li, Qiyang, et al.
Published: (2025)

TLDR: Unsupervised Goal-Conditioned RL via Temporal Distance-Aware Representations
by: Bae, Junik, et al.
Published: (2024)

OGBench: Benchmarking Offline Goal-Conditioned RL
by: Park, Seohong, et al.
Published: (2024)

METRA: Scalable Unsupervised RL with Metric-Aware Abstraction
by: Park, Seohong, et al.
Published: (2023)

Is Value Learning Really the Main Bottleneck in Offline RL?
by: Park, Seohong, et al.
Published: (2024)

Horizon Reduction Makes RL Scalable
by: Park, Seohong, et al.
Published: (2025)

Transitive RL: Value Learning via Divide and Conquer
by: Park, Seohong, et al.
Published: (2025)

Dual Goal Representations
by: Park, Seohong, et al.
Published: (2025)

Flow Q-Learning
by: Park, Seohong, et al.
Published: (2025)

Foundation Policies with Hilbert Representations
by: Park, Seohong, et al.
Published: (2024)

Intention-Conditioned Flow Occupancy Models
by: Zheng, Chongyi, et al.
Published: (2025)

Unsupervised Zero-Shot Reinforcement Learning via Functional Reward Encodings
by: Frans, Kevin, et al.
Published: (2024)

Reinforcement Learning with Action Chunking
by: Li, Qiyang, et al.
Published: (2025)

Real-Time Execution of Action Chunking Flow Policies
by: Black, Kevin, et al.
Published: (2025)

DEAS: DEtached value learning with Action Sequence for Scalable Offline RL
by: Kim, Changyeon, et al.
Published: (2025)

Adaptive Action Chunking via Multi-Chunk Q Value Estimation
by: Shin, Yongjae, et al.
Published: (2026)

Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
by: Zheng, Chongyi, et al.
Published: (2023)

Geometry-Aware Attention Guidance for Diffusion Models via Modern Hopfield Dynamics
by: Kim, Kwanyoung
Published: (2026)

Unsupervised-to-Online Reinforcement Learning
by: Kim, Junsu, et al.
Published: (2024)

Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining
by: Cheng, Jie, et al.
Published: (2024)

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
by: Nakamoto, Mitsuhiko, et al.
Published: (2023)

DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
by: Lee, Vint, et al.
Published: (2023)

Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning
by: Park, Jongchan, et al.
Published: (2025)

Functional Graphical Models: Structure Enables Offline Data-Driven Optimization
by: Kuba, Jakub Grudzien, et al.
Published: (2024)

RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes
by: Stachowicz, Kyle, et al.
Published: (2024)

Reward Sharpness-Aware Fine-Tuning for Diffusion Models
by: Kim, Kwanyoung, et al.
Published: (2026)

PLADIS: Pushing the Limits of Attention in Diffusion Models at Inference Time by Leveraging Sparsity
by: Kim, Kwanyoung, et al.
Published: (2025)

Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task
by: Kim, Yunho, et al.
Published: (2024)

Action-Free Offline-to-Online RL via Discretised State Policies
by: Neggatu, Natinael Solomon, et al.
Published: (2026)

Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization
by: Yun, Taeyoung, et al.
Published: (2024)

ViVa: Video-Trained Value Functions for Guiding Online RL from Diverse Data
by: Dashora, Nitish, et al.
Published: (2025)

Adaptive Replay Buffer for Offline-to-Online Reinforcement Learning
by: Song, Chihyeon, et al.
Published: (2025)

OMG-RL:Offline Model-based Guided Reward Learning for Heparin Treatment
by: Lim, Yooseok, et al.
Published: (2024)

Are Expressive Models Truly Necessary for Offline RL?
by: Wang, Guan, et al.
Published: (2024)

Diffusion Guidance Is a Controllable Policy Improvement Operator
by: Frans, Kevin, et al.
Published: (2025)

Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm
by: Lim, Yooseok, et al.
Published: (2024)

Budgeting Counterfactual for Offline RL
by: Liu, Yao, et al.
Published: (2023)