arXiv:2604.28123v3 Announce Type: replace-cross Abstract: The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiable rewards (RLVR). However, SFT introduces distributional drift that neither preserves the model's original capabilities nor faithfully matches the supervision distribution. This problem is further amplified in multimodal reasoning, where perception errors and reasoning failures follow distinct drift patterns that compound during subsequent RL. We introduce PR
The rapid development of large multimodal models (LMMs) is highlighting the limitations of current training paradigms, necessitating innovations in alignment techniques to improve performance and reliability.
Improving the pre-alignment of LMMs will lead to more robust, capable, and reliable AI systems, accelerating their deployment in complex real-world applications and reducing failure rates.
This research proposes a new method that could significantly enhance the training stability and performance of multimodal AI, moving beyond the standard SFT-to-RL sequence to address critical issues like distributional drift.
- · AI researchers and developers
- · Developers of multimodal AI applications
- · Industries relying on advanced AI perception and reasoning
- · Current SFT-only and basic RLVR methodologies
More efficient and effective development of powerful multimodal AI models for diverse tasks.
Accelerated integration of sophisticated multimodal AI into autonomous systems, robotics, and complex decision-making processes.
Enhanced AI capabilities lead to new breakthroughs in human-AI interaction and automation, potentially reshaping various sectors.
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Read at arXiv cs.AI