Cover Image

Divide and Conquer: Grounding LLMs as Efficient Decision-Making Agents via Offline Hierarchical Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hu, Zican, Liu, Wei, Qu, Xiaoye, Yue, Xiangyu, Chen, Chunlin, Wang, Zhi, Cheng, Yu
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Text-to-Decision Agent: Offline Meta-Reinforcement Learning from Natural Language Supervision
by: Zhang, Shilin, et al.
Published: (2025)

Attention-Guided Contrastive Role Representations for Multi-Agent Reinforcement Learning
by: Hu, Zican, et al.
Published: (2023)

Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay
by: Liu, Jinmei, et al.
Published: (2024)

Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning
by: Zhang, Weiliang, et al.
Published: (2025)

Learning to Reason under Off-Policy Guidance
by: Yan, Jianhao, et al.
Published: (2025)

Diversity-Incentivized Exploration for Versatile Reasoning
by: Hu, Zican, et al.
Published: (2025)

Mixture-of-Experts Meets In-Context Reinforcement Learning
by: Wu, Wenhao, et al.
Published: (2025)

Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale
by: Anand, Emile, et al.
Published: (2024)

Long Context Scaling: Divide and Conquer via Multi-Agent Question-driven Collaboration
by: Xiao, Sibo, et al.
Published: (2025)

Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability
by: Liang, Xiao, et al.
Published: (2026)

MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees
by: Liu, Zichuan, et al.
Published: (2022)

DCIS: Efficient Length Extrapolation of LLMs via Divide-and-Conquer Scaling Factor Search
by: Yang, Lei, et al.
Published: (2024)

FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning
by: Wang, Shang, et al.
Published: (2024)

PIANIST: Learning Partially Observable World Models with LLMs for Multi-Agent Decision Making
by: Light, Jonathan, et al.
Published: (2024)

Divide, Conquer, Combine Bayesian Decision Tree Sampling
by: Cochrane, Jodie A., et al.
Published: (2024)

Divide and Conquer: Grounding a Bleeding Areas in Gastrointestinal Image with Two-Stage Model
by: Lin, Yu-Fan, et al.
Published: (2024)

Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI
by: Wang, Chong, et al.
Published: (2024)

Praxis-VLM: Vision-Grounded Decision Making via Text-Driven Reinforcement Learning
by: Hu, Zhe, et al.
Published: (2025)

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

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning
by: Duan, Yue, et al.
Published: (2025)

Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare
by: Fang, Nan, et al.
Published: (2024)

Control Large Language Models via Divide and Conquer
by: Li, Bingxuan, et al.
Published: (2024)

Agent-as-Tool: A Study on the Hierarchical Decision Making with Reinforcement Learning
by: Zhang, Yanfei
Published: (2025)

Think Longer to Explore Deeper: Learn to Explore In-Context via Length-Incentivized Reinforcement Learning
by: Wang, Futing, et al.
Published: (2026)

Fine-grained Verbal Attack Detection via a Hierarchical Divide-and-Conquer Framework
by: Zheng, Quan, et al.
Published: (2026)

Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning
by: Röpke, Willem, et al.
Published: (2024)

DLM: Unified Decision Language Models for Offline Multi-Agent Sequential Decision Making
by: Zhang, Zhuohui, et al.
Published: (2026)

ShadowHack: Hacking Shadows via Luminance-Color Divide and Conquer
by: Hu, Jin, et al.
Published: (2024)

Single-Reset Divide & Conquer Imitation Learning
by: Chenu, Alexandre, et al.
Published: (2024)

DCR: Divide-and-Conquer Reasoning for Multi-choice Question Answering with LLMs
by: Meng, Zijie, et al.
Published: (2024)

Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning
by: Wang, Jing, et al.
Published: (2025)

Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based Medical Evaluation
by: Zheng, Shunfan, et al.
Published: (2025)

Divide-and-Conquer: Dual-Hierarchical Optimization for Semantic 4D Gaussian Spatting
by: Yan, Zhiying, et al.
Published: (2025)

A Hierarchical Divide‐and‐Conquer Neural Approach for Multiple Traveling Salesman Problems
by: Bei Ou, et al.
Published: (2026)

A Short Note on Batch-efficient Divide-and-Conquer Algorithm for EigenDecomposition
by: Song, Yue
Published: (2026)

Accurate and Scalable Matrix Mechanisms via Divide and Conquer
by: He, Guanlin, et al.
Published: (2026)

Divide-Conquer-and-Merge: Memory- and Time-Efficient Holographic Displays
by: Dong, Zhenxing, et al.
Published: (2024)

Divide and Conquer: Reliable Multi-View Evidential Learning for Deepfake Detection
by: Kang, Xiaolu, et al.
Published: (2026)

Robust and Explainable Divide-and-Conquer Learning for Intrusion Detection
by: Zhou, Yan, et al.
Published: (2026)

Intelligent Decision‐Making System of Air Defense Resource Allocation via Hierarchical Reinforcement Learning
by: Minrui Zhao, et al.
Published: (2024)