Model Alignment

🌌 Summary

What if the most valuable insights in AI evaluation aren’t in model agreements, but in systematic disagreements?

This post reveals that the “gap” between large and small reasoning models contains structured, measurable intelligence about how different architectures reason. We demonstrate how to transform model disagreements from a problem into a solution, using the space between models to make tiny networks behave more like their heavyweight counterparts.

We start by assembling a high-quality corpus (10k–50k conversation turns), score it with a local LLM to create targets, and train both HRM and Tiny models under identical conditions. Then we run fresh documents through both models, collecting not just final scores but rich auxiliary signals (uncertainty, consistency, OOD detection, etc.) and visualize what these signals reveal.

🧪 Summary

As AI developers, we often face the tradeoff between intelligence and accessibility. Powerful language models like Qwen3 run beautifully on servers but what about on the edge? On devices like Raspberry Pi or old Android phones, we’re limited to small models. The question we asked was simple:

Can we teach a small model to behave like a large one without retraining it from scratch using only its outputs and embeddings?