PyTorch's sign, round, floor, and argmax break gradients. Existing fixes (STE, Gumbel-Softmax) require rewriting your model.
SLL-Core is zero-intrusive:
with sll.linearize():
y = torch.sign(x) # now differentiable!
loss.backward()
Only linearizes inside an ε-band around decision boundaries. Everywhere else stays exactly hard. Zero deployment overhead.
pip install sll-core
