AnE: Pushing the Reasoning Frontier of Multimodal LLMs via Anchor Evolution 事件

AnE: Pushing the Reasoning Frontier of Multimodal LLMs via Anchor Evolution arXiv:2605.25571v1 Announce Type: new Abstract: Post-training via Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) is crucial for enhancing reasoning in Multimodal Large Language Models (MLLMs), yet existing paradigms often reach a performance bottleneck due to the limitations of static data. While current methods leverage self-reflection or self-evolution to push these boundaries, they still suffer from cog