Our main focus is int8 as GPU support for float8 is rare, though we also analyze float8 training through simulation.

Here,weproposea spatio-translational embeddingmodel,called BoxE,that simultaneously addresses all these limitations.

Amongst those functions, the simplest are single-index modelsf(x) = ϕ(x θ), where the labels are generated by an arbitrary non-linear scalar link functionϕ applied to an unknown one-dimensional projectionθ of the input data.

Our analysis reveals that the Polyak step-size adapts toany directional smoothness to obtain the tightest possible convergence rate.

Wefirst learn tosolvethree base tasks: collecting purple objects (Figure 5)collecting blue objects (Figure 6)andcollecting squares (Figure 7).

A major challenge is thus in designing sample-efficient agents that can transfer their existing knowledge to solve new tasks quickly.