EP360: How ARMOR stops AI reasoning collapse

Title: ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor SamplesSource: http://arxiv.org/abs/2607.10481v1Summary:This paper presents ARMOR, a novel post-training reinforcement learning framework that addresses the persistent issue of training instability and over-optimization in reasoning LLMs. By introducing active anchor rollouts from reference policies paired with a mixed optimization objective, it offers a highly effective alternative to standard KL regularization for stabilizing complex reasoning models.