# TODO-ef: Automate expanding focus in training#

If networks can’t evaluate themselves on their own predictive abilities after weight updates, it may be hard to incentivize them to explore intelligently (or at least the same way we do).

A good heuristic is to use curiosity, as discussed, but also check whether you’re continuing to learn any kind of reusable abstraction/subnet (preferably a high-value one, but you only guessed when searching in a particular direction). That is, add some kind of feedback from the training process that switches the goal of training from one task to another. See related thoughts in Expand focus.

Why do we not even automate stopping an experiment when the loss is no longer going down? Everyone seems to want to inspect the decrease in loss themselves. See Early Stopping — PyTorch Lightning 1.6.4 documentation, however.

See Transfer learning for some references to research in this direction, that is, that TL should be able to help RL.

This is not simply a workaround for limited memory. We’re actually feeding different kinds of training examples through the net, trying to use transfer learning as a kind of regularization technique.

Perhaps this is what happens between generations (throwing away heads). When one person dies we can’t take their brain’s internal representations and distribute it to others; we probably would not want to. Instead we transfer as much as we can from them through teaching, but new generations also need to take only the most reusable and most important parts from the knowledge of previous generations. For example, a younger person would be more likely (thinking long-term) to replace frequentist with bayesian thinking. Hopefully they don’t reinvent helpful math at the same time.

Should the learning rate be analogous to how you come back to planning?

Eventually add a switch-focus.md if there’s a need to address multi-tasking, continuous partial attention, etc? It may not be necessary, if you only expand your focus to the level of planning and then dig back down. See Attention span however; do children automatically try a variety of tasks in order to learn faster? When you’re training a net via transfer learning, should you rapidly switch between tasks to start, then decrease the switch rate with time?