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Unsupervised Diffusion Solver for Combinatorial Optimization via Combinatorial Adjoint Matching
Shengyu Feng, Tarun Suresh, Yiming Yang
Diffusion-based neural solvers have shown strong promise for combinatorial optimization (CO), but existing methods typically rely on supervised training with large collections of n…
Bradley-Terry Policy Optimization for Generative Preference Modeling
Shengyu Feng, Yun He, Shuang Ma +12
Reinforcement learning (RL) has recently proven effective at scaling chain-of-thought (CoT) reasoning in large language models for tasks with verifiable answers. However, extending…
SPL-LNS: Sampling-Enhanced Large Neighborhood Search for Solving Integer Linear Programs
Shengyu Feng, Zhiqing Sun, Yiming Yang
Large Neighborhood Search (LNS) is a common heuristic in combinatorial optimization that iteratively searches over a large neighborhood of the current solution for a better one. Re…
FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization
Shengyu Feng, Weiwei Sun, Shanda Li +2
Machine learning (ML) has shown promise for tackling combinatorial optimization (CO), but much of the reported progress relies on small-scale, synthetic benchmarks that fail to cap…
Regularized Langevin Dynamics for Combinatorial Optimization
Shengyu Feng, Yiming Yang
This work proposes a simple yet effective sampling framework for combinatorial optimization (CO). Our method builds on discrete Langevin dynamics (LD), an efficient gradient-guided…
SORREL: Suboptimal-Demonstration-Guided Reinforcement Learning for Learning to Branch
Shengyu Feng, Yiming Yang
Mixed Integer Linear Program (MILP) solvers are mostly built upon a Branch-and-Bound (B\&B) algorithm, where the efficiency of traditional solvers heavily depends on hand-crafted h…
Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlo
Shengyu Feng, Xiang Kong, Shuang Ma +5
Augmenting the multi-step reasoning abilities of Large Language Models (LLMs) has been a persistent challenge. Recently, verification has shown promise in improving solution consis…