  We study steering in the framework of general probabilistic theories. We show
that for dichotomic assemblages, steering can be characterized in terms of a
certain tensor cross norm, which is also related to a steering degree given by
steering robustness. Another contribution is the observation that steering in
GPTs can be conveniently treated using Choquet theory for probability measures
on the state space. In particular, we find a variational expression for
universal steering degree for dichotomic assemblages and obtain conditions
characterizing unsteerable states analogous to some conditions recently found
for the quantum case. The setting also enables us to rather easily extend the
results to infinite dimensions and arbitrary numbers of measurements with
arbitrary outcomes.
