Exploration for Building Next-Generation Foundation MLLMs via Self-Learning

ICLR 2026 Conference Submission15566 Authors

Keywords: self-learning, foundation MLLM, self-improvement, multimodal pre-training, image captioning, multimodal reasoning

TL;DR: We introduce Self-Improving cognition (SIcog), a self-learning framework for constructing next-generation foundation MLLMs by imparting multimodal knowledge and enhancing systematic cognition through multimodal pre-training with self-generated data.

Abstract: While inference-time computation and post-training optimization have significantly advanced multimodal large language models (MLLMs), these advancements remain constrained by the capabilities of foundation models. We argue that effective model advancement requires strong synergy among pre-training, inference-time computation, and post-training optimization. In this paper, we introduce Self-Improving cognition (SIcog), a self-learning framework for building next-generation foundation MLLMs by imparting multimodal knowledge and enhancing systematic cognitive capabilities through multimodal pre-training with self-generated data. Specifically, we propose Chain-of-Description for step-by-step visual understanding and integrate structured Chain-of-Thought (CoT) reasoning to support in-depth multimodal reasoning. SIcog first equips a base model with systematic perception and reasoning using minimal external supervision. The enhanced models then generate candidate image captions and CoT reasoning responses for unlabeled images and image-question pairs across diverse tasks, which are filtered through a semantic-similarity-guided self-consistency mechanism. These high-quality, self-generated samples enable large-scale multimodal pre-training, creating a self-improvement loop. Experiments demonstrate SIcog's effectiveness in developing MLLMs with enhanced multimodal cognition. Using only 213K self-generated pre-training samples, SIcog achieves significant improvements, including +3.6\% on MMStar and +3.5\% on AI2D, outperforming previous pre-training approaches. When combined with post-training techniques for CoT reasoning, SIcog yields +9\% gains on MMVet and +8.5\% on ScienceQA.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Submission Number: 15566