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Stop wasting compute on LLM evals: a variance-adaptive querying strategy slashes estimation error by focusing on the most uncertain prompt-response pairs.
You can achieve near-optimal regret in multi-armed bandits with heterogeneous noise, even without knowing the best data source *a priori*, by adaptively pruning noisy sources.
