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Stochastic bandit convex optimization is fundamentally harder than linear bandits, with a new lower bound that reveals a surprising increase in regret complexity.
Understanding how diverse SFT data can activate strategy selection reveals new pathways for enhancing model reasoning capabilities through targeted post-training interventions.
