1 citations · 1 across the 6 of their papers we have counts for
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RL Excursions during Pre-Training: Re-examining Policy Optimization for LLM training
Rachit Bansal, Clara Mohri, Tian Qin +2
The standard LLM training pipeline applies reinforcement learning (RL) only after pre-training and supervised fine-tuning (SFT). We question this status quo by training a LLM from…
Cost-Aware Learning
Clara Mohri, Amir Globerson, Haim Kaplan +2
We consider the problem of Cost-Aware Learning, where sampling different components of a finite-sum objective incurs different costs. The objective is to reach a target error while…
Budgeted Multiple-Expert Deferral
Giulia DeSalvo, Clara Mohri, Mehryar Mohri +1
Learning to defer uncertain predictions to costly experts offers a powerful strategy for improving the accuracy and efficiency of machine learning systems. However, standard traini…
Fast Inference via Hierarchical Speculative Decoding
Clara Mohri, Haim Kaplan, Tal Schuster +2
Transformer language models generate text autoregressively, making inference latency proportional to the number of tokens generated. Speculative decoding reduces this latency witho…
Mixture of Parrots: Experts improve memorization more than reasoning
Samy Jelassi, Clara Mohri, David Brandfonbrener +7
The Mixture-of-Experts (MoE) architecture enables a significant increase in the total number of model parameters with minimal computational overhead. However, it is not clear what…