15 citations · 21 across the 13 of their papers we have counts for
14 papers
Qwen-AgentWorld: Language World Models for General Agents
Yuxin Zuo, Zikai Xiao, Li Sheng +30
A world model predicts environment dynamics based on current observations and actions, serving as a core cognitive mechanism for reasoning and planning. In this work, we investigat…
Short Chains, Deep Thoughts: Balancing Reasoning Efficiency and Intra-Segment Capability via Split-Merge Optimization
Runquan Gui, Jie Wang, Zhihai Wang +3
While Large Reasoning Models (LRMs) have demonstrated impressive capabilities in solving complex tasks through the generation of long reasoning chains, this reliance on verbose gen…
HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking
Runquan Gui, Zhihai Wang, Jie Wang +7
Recent advancements have significantly enhanced the performance of large language models (LLMs) in tackling complex reasoning tasks, achieving notable success in domains like mathe…
Accelerating Large Language Model Reasoning via Speculative Search
Zhihai Wang, Jie Wang, Jilai Pan +5
Tree-search-based reasoning methods have significantly enhanced the reasoning capability of large language models (LLMs) by facilitating the exploration of multiple intermediate re…
AttentionPredictor: Temporal Patterns Matter for KV Cache Compression
Qingyue Yang, Jie Wang, Xing Li +8
With the development of large language models (LLMs), efficient inference through Key-Value (KV) cache compression has attracted considerable attention, especially for long-context…
Benchmarking End-To-End Performance of AI-Based Chip Placement Algorithms
Zhihai Wang, Zijie Geng, Zhaojie Tu +12
The increasing complexity of modern very-large-scale integration (VLSI) design highlights the significance of Electronic Design Automation (EDA) technologies. Chip placement is a c…
Learning to Cut via Hierarchical Sequence/Set Model for Efficient Mixed-Integer Programming
Jie Wang, Zhihai Wang, Xijun Li +7
Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), which formulate many important real-world applications. Cut selection heavily depends…
Learning to Stop Cut Generation for Efficient Mixed-Integer Linear Programming
Haotian Ling, Zhihai Wang, Jie Wang
Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), as they significantly tighten the dual bounds and improve the solving performance. A…
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
Xijun Li, Fangzhou Zhu, Hui-Ling Zhen +23
In an era of digital ubiquity, efficient resource management and decision-making are paramount across numerous industries. To this end, we present a comprehensive study on the inte…
Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning
Yufei Kuang, Xijun Li, Jie Wang +7
Large-scale LP problems from industry usually contain much redundancy that severely hurts the efficiency and reliability of solving LPs, making presolve (i.e., the problem simplifi…
A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design
Zhihai Wang, Lei Chen, Jie Wang +7
Logic Synthesis (LS) plays a vital role in chip design -- a cornerstone of the semiconductor industry. A key task in LS is to transform circuits -- modeled by directed acyclic grap…
Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model
Zhihai Wang, Xijun Li, Jie Wang +5
Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which formulate a wide range of important real-world applications. Cut selection -- which aim…
Efficient Exploration in Resource-Restricted Reinforcement Learning
Zhihai Wang, Taoxing Pan, Qi Zhou +1
In many real-world applications of reinforcement learning (RL), performing actions requires consuming certain types of resources that are non-replenishable in each episode. Typical…
Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic
Zhihai Wang, Jie Wang, Qi Zhou +2
Model-based reinforcement learning algorithms, which aim to learn a model of the environment to make decisions, are more sample efficient than their model-free counterparts. The sa…