Delight-gated exploration spends actions only when expected improvement times surprisal exceeds a gate price, recovers Pandora's reservation rule, and shows weaker regret growth than Thompson sampling or epsilon-greedy across bandits and MDPs with transferable hyperparameters.
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AI alignment is reframed as a fixed-point incentive problem in a solver-auditor pipeline, solved via bilevel optimization and bandit search over reward profiles to maintain monitoring and reduce hallucinations in LLM coding tasks.
citing papers explorer
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Delightful Exploration
Delight-gated exploration spends actions only when expected improvement times surprisal exceeds a gate price, recovers Pandora's reservation rule, and shows weaker regret growth than Thompson sampling or epsilon-greedy across bandits and MDPs with transferable hyperparameters.
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AI Alignment via Incentives and Correction
AI alignment is reframed as a fixed-point incentive problem in a solver-auditor pipeline, solved via bilevel optimization and bandit search over reward profiles to maintain monitoring and reduce hallucinations in LLM coding tasks.