1k citations · 1k across the 3 of their papers we have counts for
3 papers · 1 filter
Tapered Off-Policy REINFORCE: Stable and efficient reinforcement learning for LLMs
Nicolas Le Roux, Marc G. Bellemare, Jonathan Lebensold +7
We propose a new algorithm for fine-tuning large language models using reinforcement learning. Tapered Off-Policy REINFORCE (TOPR) uses an asymmetric, tapered variant of importance…
Introducing Milabench: Benchmarking Accelerators for AI
Pierre Delaunay, Xavier Bouthillier, Olivier Breuleux +12
AI workloads, particularly those driven by deep learning, are introducing novel usage patterns to high-performance computing (HPC) systems that are not comprehensively captured by…
Automatic differentiation in ML: Where we are and where we should be going
Bart van Merriënboer, Olivier Breuleux, Arnaud Bergeron +1
We review the current state of automatic differentiation (AD) for array programming in machine learning (ML), including the different approaches such as operator overloading (OO) a…