This article introduces Mixture-of-Recursions (MoR), a framework that enhances the efficiency of language models by combining parameter sharing and adaptive computation. MoR dynamically adjusts recursion depths for individual tokens, improving memory access and reducing computational costs while maintaining model performance. It shows significant improvements in validation perplexity and few-shot accuracy across various model sizes.

This article presents Render-of-Thought (RoT), a framework that converts textual reasoning steps into images to clarify the reasoning process of Large Language Models. By using existing Vision Language Models as anchors, RoT achieves significant token compression and faster inference without needing extra pre-training. Experiments show it performs competitively in reasoning tasks.