Cheking with some tests, I got to the following conclusion, which is according to the documentation and to Nassim's answer:

First, there isn't a single state in a layer, but one state per sample in the batch. There are `batch_size`

parallel states in such a layer.

### Stateful=False

In a `stateful=False`

case, all the **states are resetted together after each batch**.

A batch with `10 sequences`

would create `10 states`

, and all 10 states are resetted automatically after it's processed.

The next batch with `10 sequences`

will create `10 new states`

, which will also be resetted after this batch is processed

If all those sequences have `length (timesteps) = 7`

, the practical result of these two batches is:

20 individual sequences, each with length 7

None of the sequences are related. But of course: the weights (not the states) will be unique for the layer, and will represent what the layer has learned from all the sequences.

- A state is: Where am I now inside a sequence? Which time step is it? How is this particular sequence behaving since its beginning up to now?
- A weight is: What do I know about the general behavior of all sequences I've seen so far?

### Stateful=True

In this case, there is also the same number of parallel states, but they will **simply not be resetted at all**.

A batch with `10 sequences`

will create `10 states`

that will remain as they are at the end of the batch.

The next batch with `10 sequences`

(it's required to be 10, since the first was 10) will **reuse** the same `10 states`

that were created before.

The practical result is: the 10 sequences in the second batch are just continuing the 10 sequences of the first batch, as if there had been no interruption at all.

If each sequence has `length (timesteps) = 7`

, then the actual meaning is:

10 individual sequences, each with length 14

When you see that you reached the total length of the sequences, then you call `model.reset_states()`

, meaning you will not continue the previous sequences anymore, now you will start feeding new sequences.