Offline-Online Curriculum RL for Multimodal Reasoning
Multimodal large language models exhibit capabilities on reasoning tasks, yet often produce flawed intermediate steps while yielding correct final answers. This behavior undermines interpretability and reliability, suggesting reliance on spurious shortcuts rather than faithful reasoning. Although efforts have explored step-level supervision, distinguishing decisive steps from redundant ones remains challenging. We propose $O^2$-CritiCuRL, a novel curriculum reinforcement learning framework that introduces critical-step awareness through an iterative offline-online paradigm. In the offline stage, $O^2$-CritiCuRL conducts multi-rollout analysis over step-annotated trajectories to estimate s...
| Authors | Wendi Deng, Hang Du, Guoshun Nan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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