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arXiv Statistics ML

Optimal Stratified Allocation for Rare-Event Onset Forecasting in Dependent Sequences

The paper derives the exact finite‑population variance of a weighted risk estimator for rare‑event forecasting in dependent sequences and solves for the optimal stratified allocation of a small subsample. It shows that the optimal allocation is equal across strata, independent of the imbalance ratio, and provides a parameter‑free efficiency prediction A(π,f). The authors validate these theoretical predictions on a real‑world dataset of U.S. equities, demonstrating that the predicted ordering of sampling designs matches empirical results.

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arXiv AI
Sep 15

When Should a World Model Move? Loss-Conditioned State Execution

The paper introduces loss‑conditioned state execution, a model‑agnostic technique that decides whether to apply a world model’s proposed state change or keep the current state based on whether the change reduces downstream loss. It formalizes state movability as the existence of a loss‑reducing feasible correction and constructs loss‑specific proposals from predictive distributions, executing them only when a groupwise lower confidence bound on loss improvement is positive. Experiments on forecasting and dynamics benchmarks show that the method accepts updates for a subset of cases, achieving lower bounded loss than persistence or always executing the proposal, and highlights that event predictability and loss‑based decisions must be evaluated separately.

By Jintao Xu, Zhengyu Chen, Ben Zhang, Yongzhi Qi, Jianshen Zhang
benchmarks
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arXiv Machine Learning
Aug 28

Sequential Additivity in Distributionally Robust Ranking and Selection

The paper studies distributionally robust ranking and selection (DRR&S), where the goal is to identify the best alternative under input uncertainty by considering multiple plausible input distributions. It introduces the concept of sequential additivity, showing that efficient sampling should focus on a small, additive set of critical scenarios rather than a multiplicative number. The authors prove an algorithm‑independent lower bound on sampling, design an additive allocation (AA) procedure that meets this bound and achieves exponentially decreasing error probability, and extend the approach to a general additive allocation (GAA) framework that incorporates traditional R&S sampling rules.

By Zaile Li, Yuchen Wan, L. Jeff Hong
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arXiv Machine Learning
Jul 30

Early Verdicts, Better Budgets: Sequential Adaptive Rollout Allocation for Compute-Efficient RLVR

arXiv:2607. 26253v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is bottlenecked by rollout generation, yet many sampled prompts produce saturated groups (all responses correct or all incorrect) whose zero reward variance yields no policy-gradient signal.

By Pixel Nomand, Elena Voss, Marcus Hale, Sofia Reyes
reinforcement-learning
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