We study fixed-confidence best-arm identification under strict 1-bit feedback constraints.
At each round, the learner selects an arm and a query set, and receives only a single bit indicating whether the sampled reward belongs to that set.
We consider a distribution-free finite-variance setting with arm-wise localization, where direct empirical mean estimation is no longer available and clipping becomes unavoidable.
Methodology
We first formulate a time-uniform 1-bit mean-estimation primitive based on randomized threshold queries and a clipped tail-integral identity.
We then embed this primitive into candidate-challenger best-arm identification algorithms.
Algorithms
A fixed-clipping algorithm gives a simple anytime $(ε,δ)$-PAC guarantee, while a phased adaptive-clipping algorithm matches the clipping level to the current resolution and yields a gap-adaptive sample complexity.
Lower Bound
We also prove a $K$-arm worst-case information-theoretic lower bound showing that the logarithmic penalty caused by finite-variance 1-bit feedback is intrinsic.
This bound matches the leading dependence of the phased algorithm up to lower-order $\log\log$ factors.