17,421 research outputs found
Broken Neural Scaling Laws
We present a smoothly broken power law functional form that accurately models
and extrapolates the scaling behaviors of deep neural networks (i.e. how the
evaluation metric of interest varies as the amount of compute used for
training, number of model parameters, training dataset size, or upstream
performance varies) for various architectures and for each of various tasks
within a large and diverse set of upstream and downstream tasks, in zero-shot,
prompted, and fine-tuned settings. This set includes large-scale vision,
language, audio, video, diffusion generative modeling, multimodal learning,
contrastive learning, AI alignment, robotics, out-of-distribution
generalization, continual learning, arithmetic, unsupervised/self-supervised
learning, and reinforcement learning (single agent and multi-agent). When
compared to other functional forms for neural scaling behavior, this functional
form yields extrapolations of scaling behavior that are considerably more
accurate on this set. Moreover, this functional form accurately models and
extrapolates scaling behavior that other functional forms are incapable of
expressing such as the non-monotonic transitions present in the scaling
behavior of phenomena such as double descent and the delayed, sharp inflection
points present in the scaling behavior of tasks such as arithmetic. Lastly, we
use this functional form to glean insights about the limit of the
predictability of scaling behavior. Code is available at
https://github.com/ethancaballero/broken_neural_scaling_law
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
In this work, we propose a novel robot learning framework called Neural Task
Programming (NTP), which bridges the idea of few-shot learning from
demonstration and neural program induction. NTP takes as input a task
specification (e.g., video demonstration of a task) and recursively decomposes
it into finer sub-task specifications. These specifications are fed to a
hierarchical neural program, where bottom-level programs are callable
subroutines that interact with the environment. We validate our method in three
robot manipulation tasks. NTP achieves strong generalization across sequential
tasks that exhibit hierarchal and compositional structures. The experimental
results show that NTP learns to generalize well to- wards unseen tasks with
increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201
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