35 citations · 91 across the 14 of their papers we have counts for
6 papers · 1 filter
Training Witnesses: Trusting the Training without Trusting the Trainer
Houjun Liu, Pratyusha Sharma
Progress in machine learning cannot outpace our ability to verify it. With an explosion in papers today, every scientific claim rests initially on trust in the trainer, leading to…
LoRA vs Full Fine-tuning: An Illusion of Equivalence
Reece Shuttleworth, Jacob Andreas, Antonio Torralba +1
Fine-tuning is a crucial paradigm for adapting pre-trained large language models to downstream tasks. Recently, methods like Low-Rank Adaptation (LoRA) have been shown to effective…
The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
Pratyusha Sharma, Jordan T. Ash, Dipendra Misra
Transformer-based Large Language Models (LLMs) have become a fixture in modern machine learning. Correspondingly, significant resources are allocated towards research that aims to…
LaMPP: Language Models as Probabilistic Priors for Perception and Action
Belinda Z. Li, William Chen, Pratyusha Sharma +1
Language models trained on large text corpora encode rich distributional information about real-world environments and action sequences. This information plays a crucial role in cu…
Skill Induction and Planning with Latent Language
Pratyusha Sharma, Antonio Torralba, Jacob Andreas
We present a framework for learning hierarchical policies from demonstrations, using sparse natural language annotations to guide the discovery of reusable skills for autonomous de…
Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller
Pratyusha Sharma, Deepak Pathak, Abhinav Gupta
We study a generalized setup for learning from demonstration to build an agent that can manipulate novel objects in unseen scenarios by looking at only a single video of human demo…