arXiv Machine Learning

PathBridger: Subgoal Bridges for Offline Goal-Conditioned Reinforcement Learning

arXiv Machine Learning

Jul 24

Offline RL with Hierarchical Action Chunking

arXiv:2607. 20834v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (RL) holds the promise of learning general-purpose policies from static datasets.

By Ahad Jawaid

rag reinforcement-learning

Hugging Face Trending Papers

Jul 23

Offline RL with Hierarchical Action Chunking

Offline goal-conditioned reinforcement learning (RL) holds the promise of learning general-purpose policies from static datasets. However, scaling these methods to long-horizon tasks remains a challenge due to the curse of horizon, where value estimation errors can compound through long chains of bootstrapped Bellman backups.

rag reinforcement-learning

arXiv Machine Learning

1d ago

Recursive Value Learning for Long-Horizon Offline Goal-Conditioned RL

arXiv:2609.02237v1 Announce Type: new Abstract: Scaling offline goal-conditioned reinforcement learning (GCRL) to long-horizon tasks is difficult because (1) long-range value learning depends on shor...

By Hyeonseong Jeon, Youngwoon Lee

reinforcement-learning

arXiv Machine Learning

Aug 14

SEAR: Sample Efficient Action Chunking Reinforcement Learning

arXiv:2603. 01891v2 Announce Type: replace Abstract: Action chunking improves exploration and accelerates value propagation in long-horizon reinforcement learning, but naively applying off-policy methods to the temporally extended action space at reduced decision frequency offsets these gains, leading to poor sample efficiency.

By C. F. Maximilian Nagy, Onur Celik, Emiliyan Gospodinov, Florian Seligmann, Weiran Liao, Aryan Kaushik, Gerhard Neumann

llms rag reinforcement-learning robotics benchmarks

arXiv Machine Learning

Jun 5

Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning

arXiv:2512. 09706v2 Announce Type: replace Abstract: The paradigm of agentic AI is shifting from engineered complex workflows to post-training native models.

By Kaichen He, Zihao Wang, Muyao Li, Anji Liu, Yitao Liang

agents reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Jun 4

Dual Advantage Fields

arXiv:2606. 04188v1 Announce Type: cross Abstract: Offline goal-conditioned reinforcement learning requires both long-horizon reachability estimates and local action comparisons.

By Alexey Zemtsov, Maxim Bobrin, Alexander Nikulin, Dmitry V. Dylov, Fakhri Karray, Vladislav Kurenkov, Martin Tak\'a\v{c}, Arip Asadulaev

rag reinforcement-learning robotics safety

arXiv AI

Jul 28

Action-Sufficient Goal Representations

arXiv:2601. 22496v2 Announce Type: replace-cross Abstract: In offline goal-conditioned reinforcement learning (GCRL), hierarchical approaches decompose long-horizon tasks into high-level subgoal prediction and low-level action execution.

By Jinu Hyeon, Woobin Park, Hongjoon Ahn, Taesup Moon

reinforcement-learning

arXiv AI

Aug 18

ClawGym II: Exploring Black-Box RL on Agent Harness

arXiv:2608. 16798v1 Announce Type: cross Abstract: Agent harnesses have substantially improved performance on long-horizon tasks by coordinating agent interactions with the environment.

By Huatong Song, Fei Bai, Ming Yang, Renyuan Li, Jia Deng, Jujie He, Zhange Zhang, Daixuan Cheng, Yan Xing, Qi Yun, Xuxing Chen, Danyang Li, Feng Chang, Chuan Hao, Ran Tao, Jian Yang, Bryan Dai, Wayne Xin Zhao, Mingjie Tang, Ji-Rong Wen

llms agents reinforcement-learning

arXiv Machine Learning

Jun 18

Hierarchical Planning with Latent World Models

arXiv:2604. 03208v2 Announce Type: replace Abstract: World models are a promising path to zero-shot embodied control through planning.

By Wancong Zhang, Basile Terver, Artem Zholus, Soham Chitnis, Harsh Sutaria, Mido Assran, Randall Balestriero, Amir Bar, Adrien Bardes, Yann LeCun, Nicolas Ballas

diffusion reinforcement-learning robotics

arXiv Machine Learning

Jul 22

S3: Stable Subgoal Selection by Constraining Uncertainty of Coarse Dynamics in Hierarchical Reinforcement Learning

arXiv:2607. 19232v1 Announce Type: new Abstract: Hierarchical Reinforcement Learning (HRL) intends to separate strategic planning from primitive execution.

By Kshitij Kumar Srivastava, Kshitij Jerath

agents reinforcement-learning benchmarks

arXiv AI

Jun 16

HOLO-MPPI: Multi-Scenario Motion Planning via Hierarchical Policy Optimization

arXiv:2606. 16480v1 Announce Type: cross Abstract: Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning.

By Youngjae Min, Jovin D'sa, Faizan M. Tariq, David Isele, Navid Azizan, Sangjae Bae

agents reinforcement-learning robotics

arXiv Machine Learning

Aug 26

CoDrift: Compositional Drifting for Offline Reinforcement Learning

arXiv:2608.23939v1 Announce Type: new Abstract: Offline reinforcement learning is intrinsically multi-objective: a policy must remain compatible with the behavioral support of a fixed dataset while p...

By Xiewei Ni, Ruofeng Mei, Xiangyu Xu

reinforcement-learning benchmarks

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