I’m not entirely sure, as I haven’t experimented with such a use case, but based on this Python behavior:
model = nn.Linear(10, 10)
traced = torch.jit.script(model)

print(traced.weight)

x = torch.randn(1, 10)
out = traced(x)

with torch.no_grad():
    traced.weight.copy_(torch.ones_like(traced.weight))
out = traced(x)

I would guess you could also directly manipulate the parameters of the scripted models.
Are you able to access these parameters or are you getting an error?