is anything wrong if I change weights only when loss decreases, otherwise let them stay as they are.

```
loss_stored = 1000000000000
for i in range(10):
optimizer.zero_grad()
input = torch.randn(3, 3, requires_grad=True)
loss = model(input).abs().sum()
loss.backward()
print(list(model.parameters())[0].sum())
if loss < loss_stored:
loss_stored = loss
optimizer.step()
print(list(model.parameters())[0].sum())
print(loss)
```