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20242026
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cs.LG2026

A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training

Junze Ye, Jiayi Cheng, Miao Lu +3

For LLM agents, supervised fine-tuning is not only about teacher labels' quality, but also about which interaction contexts those labels condition on. Pure behavioral cloning uses…

cs.LG2026

Annealed Softmax Greedy in Many-Armed Bayesian Bandits

William Overman, Mohsen Bayati

Reinforcement learning with verifiable rewards and group-based policy optimization methods update a stochastic policy by sampling multiple completions per prompt and increasing the…

cs.LG2025

On Aligning Prediction Models with Clinical Experiential Learning: A Prostate Cancer Case Study

Jacqueline J. Vallon, William Overman, Wanqiao Xu +11

Over the past decade, the use of machine learning (ML) models in healthcare applications has rapidly increased. Despite high performance, modern ML models do not always capture pat…

cs.LG2024

Aligning Model Properties via Conformal Risk Control

William Overman, Jacqueline Jil Vallon, Mohsen Bayati

AI model alignment is crucial due to inadvertent biases in training data and the underspecified machine learning pipeline, where models with excellent test metrics may not meet end…

cs.LG2024

A Probabilistic Approach for Model Alignment with Human Comparisons

Junyu Cao, Mohsen Bayati

A growing trend involves integrating human knowledge into learning frameworks, leveraging subtle human feedback to refine AI models. While these approaches have shown promising res…