14 papers
LLM Priors for ERM over Programs
Shivam Singhal, Priyadarsi Mishra, Eran Malach +1
We study program-learning methods that are efficient in both samples and computation. Classical learning theory suggests that when the target admits a short program description, fo…
Distribution-Aware Algorithm Design with LLM Agents
Saharsh Koganti, Priyadarsi Mishra, Pierfrancesco Beneventano +1
Many optimization problems arise repeatedly from a fixed but unknown distribution. Even when the worst-case problem is hard, this distribution may carry reusable structure, such as…
Agentic Systems as Boosting Weak Reasoning Models
Varun Sunkaraneni, Pierfrancesco Beneventano, Riccardo Neumarker +2
Can a committee of weak reasoning-model calls reach the performance of much stronger models? We study verifier-backed committee search as inference-time boosting for reasoning lang…
Directional Neural Collapse Explains Few-Shot Transfer in Self-Supervised Learning
Achleshwar Luthra, Yash Salunkhe, Tomer Galanti
Frozen self-supervised representations often transfer well with only a few labels across many semantic tasks. We argue that a single geometric quantity, \emph{directional} CDNV (de…
DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization
Gang Li, Ming Lin, Tomer Galanti +2
The recent success and openness of DeepSeek-R1 have brought widespread attention to Group Relative Policy Optimization (GRPO) as a reinforcement learning method for large reasoning…
Self-Supervised Contrastive Learning is Approximately Supervised Contrastive Learning
Achleshwar Luthra, Tianbao Yang, Tomer Galanti
Despite its empirical success, the theoretical foundations of self-supervised contrastive learning (CL) are not yet fully established. In this work, we address this gap by showing…