Tian Ding

Knapsack RL: Unlocking Exploration of LLMs via Optimizing Budget Allocation

Bridging Formal Language with Chain-of-Thought Reasoning to Geometry Problem Solving

From Sequential to Parallel: Reformulating Dynamic Programming as GPU Kernels for Large-Scale Stochastic Combinatorial Optimization

ORGEval: Graph-Theoretic Evaluation of LLMs in Optimization Modeling

Long CoT In-Context Learning Can Empower Pre-trained LLMs

Adam-mini: Use Fewer Learning Rates To Gain More

Teaching Language Models to Reason with Tools

Self-Evolving Critique Abilities in Large Language Models

MoFO: Momentum-Filtered Optimizer for Mitigating Forgetting in LLM Fine-Tuning

Towards Explaining the Power of Constant-depth Graph Neural Networks for Structured Linear Programming

OptiBench: Benchmarking Large Language Models in Optimization Modeling with Equivalence-Detection Evaluation

On Representing Convex Quadratically Constrained Quadratic Programs via Graph Neural Networks

An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling