🏢 UC Berkeley

FAST: A novel action tokenization method using discrete cosine transform drastically improves autoregressive vision-language-action models’ training and performance, enabling dexterous and high-freque…

Toto, a new autoregressive video model, achieves competitive performance across various benchmarks by pre-training on over 1 trillion visual tokens, demonstrating the effectiveness of scaling video mo…

SWE-Gym, a novel environment for training real-world software engineering agents using 2,438 real-world Python task instances, achieves new state-of-the-art performance and is publicly available.

RAPL efficiently aligns robots with human preferences using minimal feedback by aligning visual representations before reward learning.

Predicting emergent LLM capabilities is now possible by finetuning smaller models; this approach shifts the emergence point, enabling accurate predictions of future model performance, even with up to …