Creating 2D Spatial Reasoning Data Without LLMs / VLMs: A Deterministic Curriculum Trial

Quick Links: GitHub Repository | Dataset Sample Introduction Spatial reasoning remains a significant challenge for language models, particularly in tasks requiring 2D navigation and visual-spatial understanding. Current approaches typically rely on either training large vision-language models (VLMs) on visual data or using language models to generate training examples through expensive API calls. This post explores what might be considered an unconventional (and possibly naive) approach: deterministic generation of spatial reasoning data without requiring any LLMs or VLMs in the data creation process. Rather than using models to generate training examples, this experiment algorithmically creates what we hope are realistic learning trajectories that simulate how spatial reasoning competency might develop over time. ...

January 16, 2025 | 8 min | Aman Priyanshu

Teaching AI to Read and Group Like I Bookmark the Web: A Journey into Dynamic Topic Modeling

Quick Links: Dataset on HuggingFace The Topic Modeling Challenge You know that feeling when you have 50 browser tabs open, and you’re desperately trying to organize them into bookmark folders? “ML Papers To Read,” “Funny Cat Videos,” “Recipes I’ll Never Make”… We all have our system. And apparently, it’s such a universal problem that every tech company is launching their own solution - Arc Browser with its “Spaces,” Chrome with its tab groups, and about 500 extensions promising to color-code your digital hoarding habits into submission. ...

November 11, 2024 | 4 min | Aman Priyanshu