6 papers
Fork-and-Flush: Escaping Idea Basins in Autoresearch Agents
Ziyang Cai, Christos Ziakas, Vasilis Kontonis +5
Autoresearch agents tackle open-ended problems by repeatedly proposing candidate solutions, evaluating them, and using feedback to guide subsequent experiments. We show that indepe…
JumpStart Your Policy Learning with Lessons from 160,000 Training Runs
Nabil Omi, Eric Bae, Chung Yik Edward Yeung +2
Reliable progress in offline policy learning depends on careful reporting, well-tuned baselines, and evaluation across diverse conditions. Prior work has shown that results can be…
Hierarchical Latent Prediction for Language Models
Chang Shi, Tim Pearce, Manan Tomar +2
While standard Next-Token Prediction (NTP) lays the foundation of language model pre- training, its teacher-forced training paradigm may not be optimal for long-horizon reasoning a…
Load Balancing Mixture of Experts with Similarity Preserving Routers
Nabil Omi, Siddhartha Sen, Ali Farhadi
Sparse Mixture of Experts (MoE) models offer a scalable and efficient architecture for training large neural networks by activating only a subset of parameters ("experts") for each…
Generative Modeling of Individual Behavior at Scale
Nabil Omi, Lucas Caccia, Anurag Sarkar +2
There has been a growing interest in using AI to model human behavior, particularly in domains where humans interact with this technology. While most existing work models human beh…
Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learning
Nabil Omi, Hosein Hasanbeig, Hiteshi Sharma +2
In this paper we propose a formal, model-agnostic meta-learning framework for safe reinforcement learning. Our framework is inspired by how parents safeguard their children across…