Bandits sound strictly better and usually are not. The regret-vs-inference tradeoff, the three conditions that actually favor a bandit, and the production failure that makes adaptive allocation a debugging nightmare.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
Bandits sound strictly better and usually are not. The regret-vs-inference tradeoff, the three conditions that actually favor a bandit, and the production failure that makes adaptive allocation a debugging nightmare.
The screen is whether the candidate frames it as explore-exploit regret versus clean inference, not as a buzzword preference. The reserved follow-up is that bandits optimize cumulative reward but degrade your ability to make a clean, unbiased per-arm decision afterward and complicate downstream tests, so the right tool depends on whether the customer needs to minimize cost during the test or to decide and move on; naming delayed rewards and non-stationarity as the conditions that break naive Thompson sampling is the staff tell.
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