The October 3, 2025 paper by Tencent introduces a reinforcement learning technique called Low-probability Regularization (Lp-Reg) designed to overcome the exploration collapse bottleneck in Reinforcement Learning with Verifiable Rewards (RLVR) for large language models. The authors identify that performance plateaus because training systematically eliminates crucial, low-probability tokens, termed reasoning sparks, which are necessary for diverse reasoning paths. Previous methods relying on overall policy entropy fail because they indiscriminately amplify both these valuable sparks and irrelevant noise tokens. Lp-Reg addresses this by constructing a less-noisy proxy distribution that filters out irrelevant tokens and regularizes the policy to preserve the valuable low-probability sparks, leading to stable on-policy training and achieving state-of-the-art accuracy on mathematical reasoning benchmarks. Source: https://arxiv.org/pdf/2510.03222