21 citations · 89 across the 23 of their papers we have counts for
28 papers
Outcome-Guided On-Policy Self-Distillation
ZheXu Wang, Mao-Lin Luo, Yankun Hong +6
On-policy self-distillation (OPSD) provides denser token-level supervision and better computational efficiency than Reinforcement Learning with Verifiable Rewards (RLVR). However,…
ReCAP: Retrieval-Guided Capability Reuse for Multimodal Continual Instruction Tuning
Tao Hu, Zhinuo Zhou, Xialiang Tong +2
Multimodal continual instruction tuning (MCIT) aims to enable multimodal large language models to acquire new capabilities from sequential tasks while preserving previously learned…
AIM-ZO: Activation-Informed Subspace Maintenance for Zeroth-Order LLM Fine-Tuning
Yue Xie, Zhi Zheng, Yunpeng Ba +5
Zeroth-order (ZO) optimization offers a memory-efficient alternative for LLM fine-tuning by estimating updates only from forward evaluations of perturbed parameters, without backpr…
AlgoEvo: Self-Evolving Agentic Search for Automated Algorithm Discovery
Junhao Qiu, Qinglong Hu, Xialiang Tong +4
Large language models have advanced automated algorithm discovery by synthesizing executable code, but existing frameworks trap them in rigid search pipelines with pre-defined cont…
Optimizing Denoising Trajectories in dLLMs: A Lightweight Evolutionary Heuristic Approach
Zijian Zhao, Dian Jin, Xialiang Tong +2
Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to conventional Auto-Regressive (AR) Large Language Models (LLMs). By leveraging bidirectio…
Understanding Evolution Strategies for LLM Reasoning: Broader Reasoning Coverage than GRPO
Yunpeng Ba, Zhi Zheng, Yue Xie +7
Evolution Strategies (ES) have recently emerged as a memory-efficient post-training paradigm for LLM reasoning. However, the optimization behavior of ES remains understudied, makin…
Hyper-ES: Effective Evolution Strategies for LLM Reasoning via Descent Direction Merging
Yu Gu, Zhi Zheng, Yunpeng Ba +3
Evolution Strategy (ES) is a promising alternative to gradient-based fine-tuning for resource-constrained Large Language Model (LLM) reasoning. However, directly applying ES to bil…
Beyond Average Performance: Dynamic Instance Clustering and Specialized Algorithm Design in LLM-Assisted Evolutionary Search
Qinglong Hu, Qingfu Zhang, Fei Liu +3
Large Language Model-assisted Evolutionary Search (LES) has emerged as a powerful paradigm for automated algorithm design. However, existing LES methods primarily optimize for aver…
KeepLoRA++: Continual Learning with Layer-Scaled Residual Gradient Adaptation
Mao-Lin Luo, Yi-Lin Zhang, Zi-Hao Zhou +5
Continual learning for pre-trained vision-language models requires balancing three competing objectives: retaining pre-trained knowledge, preserving knowledge from a sequence of le…
Why Attention Patterns Exist: A Unifying Temporal Perspective Analysis
Qingyue Yang, Jie Wang, Xing Li +6
Attention patterns play a crucial role in both training and inference of large language models (LLMs). Prior works have identified individual patterns such as retrieval heads, sink…
EoH-S: Evolution of Heuristic Set using LLMs for Automated Heuristic Design
Fei Liu, Yilu Liu, Qingfu Zhang +2
Automated Heuristic Design (AHD) using Large Language Models (LLMs) has achieved notable success in recent years. Despite the effectiveness of existing approaches, they only design…
Multimodal LLM-assisted Evolutionary Search for Programmatic Control Policies
Qinglong Hu, Xialiang Tong, Mingxuan Yuan +3
Deep reinforcement learning has achieved impressive success in control tasks. However, its policies, represented as opaque neural networks, are often difficult for humans to unders…
Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search
Fei Liu, Qingfu Zhang, Jialong Shi +3
Using Large Language Models (LLMs) in an evolutionary or other iterative search framework have demonstrated significant potential in automated algorithm design. However, the underl…
ARS: Automatic Routing Solver with Large Language Models
Kai Li, Fei Liu, Zhenkun Wang +4
Real-world Vehicle Routing Problems (VRPs) are characterized by a variety of practical constraints, making manual solver design both knowledge-intensive and time-consuming. Althoug…
A Systematic Survey on Large Language Models for Algorithm Design
Fei Liu, Yiming Yao, Ping Guo +9
Algorithm design is crucial for effective problem-solving across various domains. The advent of Large Language Models (LLMs) has notably enhanced the automation and innovation with…
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems
Zhi Zheng, Changliang Zhou, Tong Xialiang +2
Single-stage neural combinatorial optimization solvers have achieved near-optimal results on various small-scale combinatorial optimization (CO) problems without requiring expert k…
DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems
Zhi Zheng, Shunyu Yao, Zhenkun Wang +3
The min-max vehicle routing problem (min-max VRP) traverses all given customers by assigning several routes and aims to minimize the length of the longest route. Recently, reinforc…
Prompt Learning for Generalized Vehicle Routing
Fei Liu, Xi Lin, Weiduo Liao +4
Neural combinatorial optimization (NCO) is a promising learning-based approach to solving various vehicle routing problems without much manual algorithm design. However, the curren…
Self-Improved Learning for Scalable Neural Combinatorial Optimization
Fu Luo, Xi Lin, Zhenkun Wang +3
The end-to-end neural combinatorial optimization (NCO) method shows promising performance in solving complex combinatorial optimization problems without the need for expert design.…
Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solver
Changliang Zhou, Xi Lin, Zhenkun Wang +3
In modern intelligent transportation systems (ITS), particularly in freight transportation and logistics, real-time route planning is crucial. It presents unique challenges driven…
Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization
Fei Liu, Xi Lin, Zhenkun Wang +3
Vehicle routing problems (VRPs), which can be found in numerous real-world applications, have been an important research topic for several decades. Recently, the neural combinatori…
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model
Fei Liu, Xialiang Tong, Mingxuan Yuan +5
Heuristics are widely used for dealing with complex search and optimization problems. However, manual design of heuristics can be often very labour extensive and requires rich work…
Algorithm Evolution Using Large Language Model
Fei Liu, Xialiang Tong, Mingxuan Yuan +1
Optimization can be found in many real-life applications. Designing an effective algorithm for a specific optimization problem typically requires a tedious amount of effort from hu…
Large Language Model for Multi-objective Evolutionary Optimization
Fei Liu, Xi Lin, Zhenkun Wang +4
Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of whic…