9 papers
LANPO: Bootstrapping Language and Numerical Feedback for Reinforcement Learning in LLMs
Ang Li, Yifei Wang, Zhihang Yuan +2
Reinforcement learning in large language models (LLMs) often relies on scalar rewards, a practice that discards valuable textual rationale buried in the rollouts, forcing the model…
Geometric Algorithms for Neural Combinatorial Optimization with Constraints
Nikolaos Karalias, Akbar Rafiey, Yifei Xu +4
Self-Supervised Learning (SSL) for Combinatorial Optimization (CO) is an emerging paradigm for solving combinatorial problems using neural networks. In this paper, we address a cen…
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning
Xiaojun Guo, Ang Li, Yifei Wang +2
Although Large Language Models (LLMs) have demonstrated remarkable progress, their proficiency in graph-related tasks remains notably limited, hindering the development of truly ge…
Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation
Tiansheng Wen, Yifei Wang, Zequn Zeng +7
Many large-scale systems rely on high-quality deep representations (embeddings) to facilitate tasks like retrieval, search, and generative modeling. Matryoshka Representation Learn…
Learning with Exact Invariances in Polynomial Time
Ashkan Soleymani, Behrooz Tahmasebi, Stefanie Jegelka +1
We study the statistical-computational trade-offs for learning with exact invariances (or symmetries) using kernel regression. Traditional methods, such as data augmentation, group…
Subsampling Graphs with GNN Performance Guarantees
Mika Sarkin Jain, Stefanie Jegelka, Ishani Karmarkar +2
How can we subsample graph data so that a graph neural network (GNN) trained on the subsample achieves performance comparable to training on the full dataset? This question is of f…