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Self-Routing is proposed, a behavior-conditioned post-training framework that uses rollout correctness and confidence to decide how each sample should be optimized, allowing training to adapt without external teachers, extra annotations, or additional sampling.
This work proposes $O^2-CritiCuRL, a novel curriculum reinforcement learning framework that introduces critical-step awareness through an iterative offline-online paradigm, and employs a progressive step-level reinforcement learning strategy, where truncated chains guide the model to infer missing steps and refine its reasoning.
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