1 citations · 2 across the 11 of their papers we have counts for
6 papers · 1 filter
ReAPO: Retry-Aware Policy Optimization for Efficient Exploration in LLM Reasoning
Nghi Dao, Calder Morton-Ferguson, Noah Golowich +1
RLVR improves language-model reasoning, but commonly relies on independent stochastic rollouts for exploration, which can repeatedly sample similar failed strategies. Prior work ha…
MoSAIK: Motion Search with Artificial Intelligence in K-space
Oscar Dabrowski, Abijith Jagannath, Frédéric Grouiller +1
Object: Motion artefacts remain a major limitation of routine two-dimensional T2-weighted turbo spin-echo (TSE) brain MRI, reducing diagnostic confidence and often necessitating re…
LLM Evaluation is a Forecasting Problem
Rahul Thomas, Adam Block, Micah Goldblum
Standard procedure in machine learning is to randomly split train and test data, emulating IID draws from one underlying data distribution. In practice, models are trained on past…
Training for the Model You Return: Improving Optimization for Iterate-Averaged Language Models
Kwok Chun Au, Adam Block
Many modern Language Model (LM) pipelines return an averaged model, such as an exponential moving average of the training iterates, rather than the final iterate itself. This raise…
Behavior Cloning is Not All You Need: The Optimality of On-Policy Distillation for Noisy Expert Feedback
Ved Sriraman, Peihan Liu, Daniel Hsu +1
Imitation Learning is a natural framework for learning in sequential decision-making systems and has emerged as the dominant paradigm through which we understand language model tra…
Revisiting the (Sub)Optimality of Best-of-N for Inference-Time Alignment
Ved Sriraman, Adam Block
Best-of-N (BoN) sampling is a widely used inference-time alignment method for language models, whereby N candidate responses are sampled from a reference model and the one with the…