Categories: AI/ML News

Multimodal and reasoning LLMs supersize training data for dexterous robotic tasks

For robots, simulation is a great teacher for learning long-horizon (multi-step) tasks—especially compared to how long it takes to collect real-world training data.

RT-1: Robotics Transformer for Real-World Control at Scale

December 14, 2022

In "FAANG"

When can transformers reason with abstract symbols?

We investigate the capabilities of transformer models on relational reasoning tasks. In these tasks, models are trained on a set of strings encoding abstract relations, and are then tested out-of-distribution on data that contains symbols that did not appear in the training dataset. We prove that for any relational reasoning…

May 2, 2024

In "FAANG"

Exploring Prediction Targets in Masked Pre-Training for Speech Foundation Models

Speech foundation models, such as HuBERT and its variants, are pre-trained on large amounts of unlabeled speech data and then used for a range of downstream tasks. These models use a masked prediction objective, where the model learns to predict information about masked input segments from the unmasked context. The…

March 14, 2025

In "FAANG"

AI Generated Robotic Content