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cs.LG2026
Two Clocks: Why Larger Rollout Batches Learn Slower but Fail on Schedule in Reinforcement Learning with Verifiable Rewards
Yuante Li, Junwei Quan, Kai Qiu +2
Reinforcement learning with verifiable rewards (RLVR) relies on costly model-generated responses, motivating larger rollout batches and square-root learning-rate scaling. Reported…
cs.LG2022
Learning Sparsity and Randomness for Data-driven Low Rank Approximation
Tiejin Chen, Yicheng Tao
Learning-based low rank approximation algorithms can significantly improve the performance of randomized low rank approximation with sketch matrix. With the learned value and fixed…