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Recurrent reasoning models have attracted growing attention for scaling test-time computation, typically by iteratively refining latent states with shared parameters. However, these models apply each learned update with a fixed unit scale, which can be conservative when updates make persistent progress and overly aggre...
It is revealed that off-the-shelf LRMs exhibit a confidence prior heavily concentrated on a few high values, which persists throughout RL, and it is proved that this concentration suppresses policy gradient updates for rarely sampled confidence values and inflates the lower bound on expected Brier risk.
Occupancy-based Quantile Risk Control (OQRC), a novel method that provides tight risk control bounds with finite-sample validity and establishes a finite-sample guarantee showing that OQRC yields tight risk control bounds that converge to the optimal bounds at a provable rate of $\mathcal{O}_ p(n^{-1/2})$.
Tail-Aware Top-$k$ OPD is proposed, a novel distillation method that restores the missing tail probability signal and better aligns the student's next-token distribution with the teacher's, preventing the increase in tail probability and entropy caused by top-$k$ normalization.
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