Gordon & AIly
AIly
Hey Gordon, I've been running some simulations on scheduling neural‑network training epochs to minimize convergence time while maximizing generalization. Do you think there's a principled way to balance exploration and exploitation in that context?
Gordon
You can frame the epoch‑scheduling problem as a multi‑armed bandit: each “arm” is a particular schedule of learning‑rate decay or number of epochs. Then use an exploration‑exploitation strategy such as UCB or Thompson sampling to decide which schedule to try next. In practice you monitor validation loss and, when the loss plateaus, shift toward more aggressive schedules (exploitation) but keep a small probability of sampling a different schedule (exploration). That keeps the training balanced between converging quickly and avoiding overfitting.
AIly
That’s a solid framework. I’d add a small, fixed‑interval checkpoint every few epochs to log validation curves; that gives a quick sanity check on the exploration step. Also, if you keep a rolling average of the loss improvements, you can trigger a shift in the schedule when the variance drops below a threshold. How do you usually decide the initial arm distribution?
Gordon
I usually start with a uniform distribution over a sensible set of schedules, then bias toward the ones that historically performed well on similar architectures. If I have prior data, I weight those arms higher; otherwise, keep it flat and let the bandit learn from scratch. That way the initial exploration covers the space evenly without wasting time on obviously bad choices.
AIly
Sounds efficient—just make sure you log the reward metrics so the bandit can update the priors quickly. A tiny random seed shuffle each run keeps the system from getting stuck in a local optimum. Good luck!
Gordon
That’s the idea—quick logging and a tiny seed shuffle will keep the policy from drifting into a single local optimum. Thanks for the tip.
AIly
Glad it helps! Keep a tidy log of every run and you’ll catch drift before it happens. Good luck tuning!