  In this paper, we introduce the first algorithmic framework for Blackwell
approachability on the sequence-form polytope, the class of convex polytopes
capturing the strategies of players in extensive-form games (EFGs). This leads
to a new class of regret-minimization algorithms that are stepsize-invariant,
in the same sense as the Regret Matching and Regret Matching$^+$ algorithms for
the simplex. Our modular framework can be combined with any existing regret
minimizer over cones to compute a Nash equilibrium in two-player zero-sum EFGs
with perfect recall, through the self-play framework. Leveraging predictive
online mirror descent, we introduce Predictive Treeplex Blackwell$^+$
(PTB$^+$), and show a $O(1/\sqrt{T})$ convergence rate to Nash equilibrium in
self-play. We then show how to stabilize PTB$^+$ with a stepsize, resulting in
an algorithm with a state-of-the-art $O(1/T)$ convergence rate. We provide an
extensive set of experiments to compare our framework with several algorithmic
benchmarks, including CFR$^+$ and its predictive variant, and we highlight
interesting connections between practical performance and the
stepsize-dependence or stepsize-invariance properties of classical algorithms.
