AI RESEARCH
How RL Unlocks the Aha Moment in Geometric Interleaved Reasoning
arXiv CS.CL
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ArXi:2603.01070v2 Announce Type: replace Solving complex geometric problems inherently requires interleaved reasoning: a tight alternation between constructing diagrams and performing logical deductions. Although recent Multimodal Large Language Models (MLLMs) have nstrated strong capabilities in visual generation and plotting, we identify a counter-intuitive and underexplored phenomenon. Naively applying Supervised Fine-Tuning (SFT) on interleaved plot-solution data leads to a substantial degradation in reasoning performance compared to text-only baselines.