68 citations · 75 across the 11 of their papers we have counts for
5 papers · 1 filter
PoEM: Predicting RL Outcomes from Existing Policies
Kimia Hamidieh, Giannis Daras, Antonio Torralba
Foundation models are post-trained with reinforcement learning (RL) to maximize specific rewards, such as human alignment, correctness, or instruction following. This post-training…
MatrAIx: Simulating the World with 8.3 Billion Persona Agents
Xiaomin Li, Yuexing Hao, Jianheng Hou +90
Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and inter…
Bridging Compute- and Data-Optimal Pretraining
Tian Qin, Kimia Hamidieh, David Alvarez-Melis
Classical compute-optimal scaling laws assume an unbounded supply of fresh pretraining data, yet pretraining is increasingly entering a regime in which compute grows faster than th…
Domain-Aware Scaling Laws Uncover Data Synergy
Kimia Hamidieh, Lester Mackey, David Alvarez-Melis
Machine learning progress is often attributed to scaling model size and dataset volume, yet the composition of data can be just as consequential. Empirical findings repeatedly show…
Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
Kimia Hamidieh, Veronika Thost, Walter Gerych +2
Large language models (LLMs) often produce confident yet incorrect responses, and uncertainty quantification is one potential solution to more robust usage. Recent works routinely…