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End-to-end training is best suited to settings where resources are genuinely scarce and feasibility matters, and leads the delay-adjusted policy value wherever capacity is scarce, once a waiting period carries even a small cost.
Test-time scaling has emerged as a key inference strategy for reasoning models, where a model generates a large number of candidate solutions in the hope of finding at least one correct solution. Running all candidate solutions to completion can be computationally demanding, so recent work prunes the candidate pool aft...
A strong, fixed, rule-based expert is built for Gin Rummy and used only as a yardstick, never for training, and the result is a lightweight, game-agnostic recipe that trains competitive agents without training on the expert, for any game a small model can handle, reported with robust statistics and released as a reusab...
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