BitNet: Scaling 1-bit Transformers for Large Language Models

The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption. In this work, we introduce BitNet, a scalable and stable 1-bit Transformer architecture designed for large language models. Specifically, we introduce BitLinear as a drop-in replacement of the nn.Linear layer in order to train 1-bit weights from scratch. Experimental results on language modeling show that BitNet achieves competitive performance while substantially reducing memory footprint and energy consumption, compared to state-of-the-art 8-bit quantization methods and FP16 Transformer baselines. Furthermore, BitNet exhibits a scaling law akin to full-precision Transformers, suggesting its potential for effective scaling to even larger language models while maintaining efficiency and performance benefits.

Language-Guided Generation of Physically Realistic Robot Motion and Control

We aim to control a robot to physically behave in the real world following any high-level language command like "cartwheel" or "kick. " Although human motion datasets exist, this task remains particularly challenging since generative models can produce physically unrealistic motions, which will be more severe for robots due to different body structures and physical properties. In addition, to control a physical robot to perform a desired motion, a control policy must be learned. We develop LAnguage-Guided mOtion cONtrol (LAGOON), a multi-phase method to generate physically realistic robot motions under language commands. LAGOON first leverages a pre-trained model to generate human motion from a language command. Then an RL phase is adopted to train a control policy in simulation to mimic the generated human motion. Finally, with domain randomization, we show that our learned policy can be successfully deployed to a quadrupedal robot, leading to a robot dog that can stand up and wave its front legs in the real world to mimic the behavior of a hand-waving human.