The recent introduction of VCORE, a new method for variance-controlled optimization-based reweighting, marks a significant advancement in the field of supervised fine-tuning for large language models. This approach addresses the limitations of traditional methods by recognizing that not all tokens in a reasoning trajectory contribute equally to the learning process. By improving how models are trained on complex reasoning tasks, VCORE promises to enhance the overall reasoning capabilities of these models, making them more effective in real-world applications.