Mixture of Experts (MoE) is a neural network design where, instead of every parameter working on every input, a "router" sends each piece of data to a small handful of specialist sub-networks called experts — so a model can carry hundreds of billions of parameters in storage while only switching on a sliver of them for any single token or image. That single idea is why some of today's most capable open and commercial models can hold enormous knowledge without needing enormous compute for every request. 🧠 This matters because the old way of scaling — just making one dense network bigger — hits a wall: compute cost, energy draw, and inference latency all grow in lockstep with parameter count. MoE breaks that lockstep, and it now shows up across nearly every corner of AI: general chat models, translation systems, vision backbones, vision-language models, and even research on scientific computing. Teams at Google, Mistral AI, Databricks, DeepSeek, Alibaba, Meta, and Micro...

Imagine two ways of getting somewhere: a paper map you printed once and follow exactly, or a GPS app that watches the road as you drive and reroutes you the moment something changes. That difference — plan-once-and-follow versus watch-and-adjust-in-real-time — is almost exactly the difference between how AI reasoning used to be tested, and how the newest reasoning systems actually work today. This post uses that one idea throughout, so by the end, "static evaluation" and "dynamic self-correction" won't feel like jargon anymore — they'll just feel like the map versus the GPS. 🗺️ 🗺️ Static Evaluation (the paper map) 📍 Dynamic Self-Correction (the live GPS) When it decides Everything is fixed before the trip starts. Keeps deciding as new information arrives. What happens to a mistake A wrong turn early on stays wrong for the rest of the trip. A wrong turn gets noticed and corrected mid-route. Cost Cheap and instant — just read the map ...

Training a reasoning model well is only half the job — you also need a reliable way to check whether it's reasoning well, and a safe way to let it fix its own mistakes. This post covers four pieces of that puzzle: how SFT and RL each shape reasoning behavior during training, how "LLM-as-a-judge" frameworks grade a reasoning trace, how evaluator-verifier workflows turn that grading into a trustworthy reliability score, and why letting a model correct itself in production needs guardrails before it's safe to ship. 🎯 1. How SFT and RL Improve Reasoning Behavior During Training 🧩 Beginner Primer: What SFT and RL Actually Mean SFT (Supervised Fine-Tuning) is learning by studying solved examples — like studying worked-out problems in a textbook's answer key before an exam. You take a general-purpose model and show it thousands of high-quality question-and-answer pairs, each one written or approved by an expert. The model adjusts itself to imitate that pattern: ...

A "reasoning model" doesn't just answer a question — it thinks its way there, step by step, before committing to an answer. That extra thinking is powerful, but it also creates a failure mode most beginners never hear about: one small slip early in the chain can quietly wreck everything that follows. This post covers what reasoning models are, why a single bad step can sink the whole answer, where these models are actually being used today, and where accuracy and training really matter. 🎯 1. What Are "Reasoning Models"? Here's the core idea before anything else: a reasoning model isn't a static predictor that maps a fixed input to a fixed output in one calculation, the way a calculator plugs numbers into a formula. It's a dynamically evolving inference process — the model generates one token, feeds that token back in as part of its own input, and then generates the next token based on everything decided so far. Because of this, every token it p...