Chunk-Guided Q-Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Song, Gwanwoo, Park, Kwanyoung, Lee, Youngwoon |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| 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)
by: Park, Kwanyoung, et al.
Published: (2024)
Scalable Offline Model-Based RL with Action Chunks
by: Park, Kwanyoung, et al.
Published: (2025)
by: Park, Kwanyoung, et al.
Published: (2025)
TLDR: Unsupervised Goal-Conditioned RL via Temporal Distance-Aware Representations
by: Bae, Junik, et al.
Published: (2024)
by: Bae, Junik, et al.
Published: (2024)
Decoupled Q-Chunking
by: Li, Qiyang, et al.
Published: (2025)
by: Li, Qiyang, et al.
Published: (2025)
Adaptive Action Chunking via Multi-Chunk Q Value Estimation
by: Shin, Yongjae, et al.
Published: (2026)
by: Shin, Yongjae, et al.
Published: (2026)
DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
by: Lee, Vint, et al.
Published: (2023)
by: Lee, Vint, et al.
Published: (2023)
Geometry-Aware Attention Guidance for Diffusion Models via Modern Hopfield Dynamics
by: Kim, Kwanyoung
Published: (2026)
by: Kim, Kwanyoung
Published: (2026)
Bridging Domain Gaps with Target-Aligned Generation for Offline Reinforcement Learning
by: Kim, Minung, et al.
Published: (2026)
by: Kim, Minung, et al.
Published: (2026)
Reward Sharpness-Aware Fine-Tuning for Diffusion Models
by: Kim, Kwanyoung, et al.
Published: (2026)
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)
by: Kim, Kwanyoung, et al.
Published: (2025)
Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning
by: Park, Jongchan, et al.
Published: (2025)
by: Park, Jongchan, et al.
Published: (2025)
Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling
by: Liu, Yuejiang, et al.
Published: (2024)
by: Liu, Yuejiang, et al.
Published: (2024)
Reinforcement Learning with Action Chunking
by: Li, Qiyang, et al.
Published: (2025)
by: Li, Qiyang, et al.
Published: (2025)
Flow Q-Learning
by: Park, Seohong, et al.
Published: (2025)
by: Park, Seohong, et al.
Published: (2025)
Soft-NBCE: Entropy-Weighted Chunk Fusion for Long-Context
by: Ji, Shihao, et al.
Published: (2026)
by: Ji, Shihao, et al.
Published: (2026)
HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation
by: Sferrazza, Carmelo, et al.
Published: (2024)
by: Sferrazza, Carmelo, et al.
Published: (2024)
ScaleDiff: Higher-Resolution Image Synthesis via Efficient and Model-Agnostic Diffusion
by: Koh, Sungho, et al.
Published: (2025)
by: Koh, Sungho, et al.
Published: (2025)
Frictional Q-Learning
by: Kim, Hyunwoo, et al.
Published: (2025)
by: Kim, Hyunwoo, et al.
Published: (2025)
Concept-Guided Interpretability via Neural Chunking
by: Wu, Shuchen, et al.
Published: (2025)
by: Wu, Shuchen, et al.
Published: (2025)
Chunking Strategies for Multimodal AI Systems
by: R, Shashanka B, et al.
Published: (2025)
by: R, Shashanka B, et al.
Published: (2025)
Discovering Chunks in Neural Embeddings for Interpretability
by: Wu, Shuchen, et al.
Published: (2025)
by: Wu, Shuchen, et al.
Published: (2025)
Suppressing Overestimation in Q-Learning through Adversarial Behaviors
by: Lee, HyeAnn, et al.
Published: (2023)
by: Lee, HyeAnn, et al.
Published: (2023)
Periodic Regularized Q-Learning
by: Yang, Hyukjun, et al.
Published: (2026)
by: Yang, Hyukjun, et al.
Published: (2026)
Adaptive Replay Buffer for Offline-to-Online Reinforcement Learning
by: Song, Chihyeon, et al.
Published: (2025)
by: Song, Chihyeon, et al.
Published: (2025)
Safe-Support Q-Learning: Learning without Unsafe Exploration
by: Lim, Yeeun, et al.
Published: (2026)
by: Lim, Yeeun, et al.
Published: (2026)
Temporal Chunking Enhances Recognition of Implicit Sequential Patterns
by: Dey, Jayanta, et al.
Published: (2025)
by: Dey, Jayanta, et al.
Published: (2025)
Curriculum Guided Personalized Subgraph Federated Learning
by: Kang, Minku, et al.
Published: (2025)
by: Kang, Minku, et al.
Published: (2025)
Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding
by: Tripathi, Vishesh, et al.
Published: (2025)
by: Tripathi, Vishesh, et al.
Published: (2025)
Q-Palette: Fractional-Bit Quantizers Toward Optimal Bit Allocation for Efficient LLM Deployment
by: Lee, Deokjae, et al.
Published: (2025)
by: Lee, Deokjae, et al.
Published: (2025)
Cognitive Chunking for Soft Prompts: Accelerating Compressor Learning via Block-wise Causal Masking
by: Liu, Guojie, et al.
Published: (2026)
by: Liu, Guojie, et al.
Published: (2026)
In-Context Compositional Q-Learning for Offline Reinforcement Learning
by: Xu, Qiushui, et al.
Published: (2025)
by: Xu, Qiushui, et al.
Published: (2025)
Learning the Model While Learning Q: Finite-Time Sample Complexity of Online SyncMBQ
by: Lim, Han-Dong, et al.
Published: (2024)
by: Lim, Han-Dong, et al.
Published: (2024)
From Reward Shaping to Q-Shaping: Achieving Unbiased Learning with LLM-Guided Knowledge
by: Wu, Xiefeng
Published: (2024)
by: Wu, Xiefeng
Published: (2024)
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning
by: Lee, Dohyeok, et al.
Published: (2024)
by: Lee, Dohyeok, et al.
Published: (2024)
Diffusion Fine-Tuning via Reparameterized Policy Gradient of the Soft Q-Function
by: Kang, Hyeongyu, et al.
Published: (2025)
by: Kang, Hyeongyu, et al.
Published: (2025)
FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning
by: Alles, Marvin, et al.
Published: (2025)
by: Alles, Marvin, et al.
Published: (2025)
UNICORN: Ultrasound Nakagami Imaging via Score Matching and Adaptation
by: Kim, Kwanyoung, et al.
Published: (2024)
by: Kim, Kwanyoung, et al.
Published: (2024)
Exclusively Penalized Q-learning for Offline Reinforcement Learning
by: Yeom, Junghyuk, et al.
Published: (2024)
by: Yeom, Junghyuk, et al.
Published: (2024)
OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
by: Kim, Kwanyoung, et al.
Published: (2024)
by: Kim, Kwanyoung, et al.
Published: (2024)
Training Long-Context LLMs Efficiently via Chunk-wise Optimization
by: Li, Wenhao, et al.
Published: (2025)
by: Li, Wenhao, et al.
Published: (2025)
Similar Items
-
Model-based Offline Reinforcement Learning with Lower Expectile Q-Learning
by: Park, Kwanyoung, et al.
Published: (2024) -
Scalable Offline Model-Based RL with Action Chunks
by: Park, Kwanyoung, et al.
Published: (2025) -
TLDR: Unsupervised Goal-Conditioned RL via Temporal Distance-Aware Representations
by: Bae, Junik, et al.
Published: (2024) -
Decoupled Q-Chunking
by: Li, Qiyang, et al.
Published: (2025) -
Adaptive Action Chunking via Multi-Chunk Q Value Estimation
by: Shin, Yongjae, et al.
Published: (2026)