Yes, in my experience results look very similar with almost any sensible approach.
An example of this t-2 thing I commented above could be the DeepAR paper: https://arxiv.org/abs/1704.04110 9
Other people actually feed t-1 twice: once as the las timestep for the encoder and again as the first timestep for the decoder. This is the approach used by the winner of this Kaggle competition back in 2017: https://www.kaggle.com/c/web-traffic-time-series-forecasting/discussion/43795 25
As for feeding the encoder’s last hidden state as input for all decoder timesteps, the most popular example is in Guillaume Chevalier’s great tutorial on signal prediction with seq2seq models: https://github.com/guillaume-chevalier/seq2seq-signal-prediction 43
After trying these different approaches, my conclusion is that they pretty much all work and yield very similar results on a wide range of experiments and datasets (Airline Passengers dataset, m4 competition…). It looks like that as long as you have the appropiate number of weights and your loss makes sense, the network figures out the best way to deal with your architectural choice as long as it is sensible, like the three described in these paragraphs.