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

Post-training via Supervised Fine-Tuning (SFT) and Rein-forcement Learning (RL) is crucial for enhancing reasoning in Multi-modal Large Language Models (MLLMs), yet existing paradigms of-ten reach a performance bottleneck due to the limitations of staticdata. While current methods leverage self-reflection or self-evolution topush these boundaries, they still suffer from cognitive drift and hallu-cinated reasoning paths caused by low-quality synthetic data. To ad-dress these challenges, we propose Anchor E volution (AnE), a newparadigm that integrates truth-anchored data curation and model evo-lution, achieving faithful and steady performance gains at the reason-ing frontier. Specifically, we propose Truth Anchor Expansion, whichpinpoints the model failing frontier via trajectory rollouts and lever-ages ground-truth databases to retrieve high-fidelity anchors for faithfuldata curation. Subsequently, we introduce the Scaffold-Stripping Mech-anism to internalize reasoning capabilities. This mechanism first an-chors reasoning paths via scaffold-augmented supervision to mitigatethe learning complexity and distribution drift of direct SFT on rawdata, then leverages RL to strip the scaffold template, thereby effec-tively transitioning the reasoning paths into intrinsic model capabilities.Experimental results on multimodal reasoning benchmarks show thatour method substantially advances the model performance frontier, im-proving the base model by 10.3% across eight multimodal benchmarksand achieving state-of-the-art results. The code will be publicly availableat https://github.com/wangzehao0704/AnE.