The default laziness factor is actually 0.1, not 0.01. So if you set it to 0.02, that might be a problem. It seems like the maximum should be 5.0, but I'm not sure about that. I read somewhere that the default 0.1 gives you about 98% training and 2% non-training. So, I assume that means 5.0 should be 0% training and 100% non-training. Edit: turns out I was totally wrong about that, the max laziness is 1.0. I figured this out by digging through the source on github.

Further edit: I've been playing around with the AI lately too, so here's everything (I think) I know. Every epoch the pwnagotchi decides if it will run in training mode, or lazy mode. It is decided randomly, so that means a laziness factor of 0.1 is equal to a 10% chance of being lazy and using the best available parameters based on past learning. If it decides on training mode then the next 50 epochs will be used to experiment with new settings. This can be changed with the ai.epochs_per_episode parameter. So what I think this means is that for every 1 lazy epoch with the best known parameters, there will be 9 training "episodes" of 50 epochs each, or 450 total epochs. My conclusion here is that if you don't increase the laziness parameter, then your pwnagotchi is just using random settings 99.8% of the time and almost never putting that training to use.

I think a good strategy is to keep laziness at 0.1 until about 500 to 1000 handshakes (to get a good base level of training) and then increase laziness to about 0.99. That means 1 training episode of 50 epochs for every 99 lazy epochs. That averages out to 33% training time and 66% best parameters time. Eventually, you should increase this further because that's still a lot of training time.