Mind's Eye: Grounded Language Model Reasoning through Simulation
Improves reasoning ability using MuJoCo simulations by a large margin (+27.9/46.0% zero/few-shot absolute acc. on average). LMs + Mind's Eye performs on par with 100x larger models.
Businesses can use this approach to create AI models that reason more effectively and accurately, improving decision-making processes and outcomes. This can lead to increased efficiency and productivity, as well as improved customer experiences.
Self-supervised video pretraining yields strong image representations
For the first time, our video-pretrained model closes the gap with ImageNet pretraining, suggesting that video-pretraining could become the new default for image representations.
Businesses can use video-pretraining to improve their image recognition capabilities, which can be useful in a variety of areas such as object detection, security, and product recommendation systems. This can lead to more accurate and efficient operations and improved customer experiences.
Foundation Transformers
Proposes Sub-LayerNorm for good expressivity, and the initialization strategy theoretically derived from DeepNet for stable scaling up.
Businesses can use Foundation Transformers to create general-purpose AI models that can be applied to various tasks and modalities with guaranteed training stability. This can lead to more efficient and accurate operations, as well as improved outcomes.
Automatic Discovery of Composite SPMD Partitioning Strategies in PartIR
This research presents an automatic partitioner that identifies efficient combinations of advanced parallelism strategies for many model architectures and accelerator systems through a goal-oriented search.
Implementing an automatic partitioner in your business operations can streamline the process of identifying efficient combinations for training large neural network models, which can improve overall productivity and save time and resources.