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About this paper
Naoki Nishikawa, Taiji Suzuki
The authors analyze adaptive reward querying in a hierarchical model of reasoning tasks. Deeper reward components become visible only after earlier prefixes identify promising regions, letting the current policy guide later feedback. At one prompt and zero regularization, on-policy regret has a polynomial rate, while fixed uniform response sampling has tight logarithmic regret decay. These are theoretical query results, not benchmark measurements.
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