5 papers
Speculative Speculative Decoding
Tanishq Kumar, Tri Dao, Avner May
Autoregressive decoding is bottlenecked by its sequential nature. Speculative decoding has become a standard way to accelerate inference by using a fast draft model to predict upco…
Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap
Mengmi Zhang, Elisa Pavarino, Xiao Liu +20
As AI becomes increasingly embedded in daily life, ascertaining whether an agent is human is critical. We systematically benchmark AI's ability to imitate humans in three language…
Overtrained Language Models Are Harder to Fine-Tune
Jacob Mitchell Springer, Sachin Goyal, Kaiyue Wen +5
Large language models are pre-trained on ever-growing token budgets under the assumption that better pre-training performance translates to improved downstream models. In this work…
Scaling Laws for Precision
Tanishq Kumar, Zachary Ankner, Benjamin F. Spector +6
Low precision training and inference affect both the quality and cost of language models, but current scaling laws do not account for this. In this work, we devise "precision-aware…
Do Mice Grok? Glimpses of Hidden Progress During Overtraining in Sensory Cortex
Tanishq Kumar, Blake Bordelon, Cengiz Pehlevan +2
Does learning of task-relevant representations stop when behavior stops changing? Motivated by recent theoretical advances in machine learning and the intuitive observation that hu…