distilled

Distilled Weekly — Feb 23 - Mar 01, 2026

Santthosh Selvadurai

01 Mar 2026 — 1 min read

Welcome to this week's Distilled! We've got a fascinating pair of papers that tackle two of AI's biggest practical challenges: helping robots actually learn on the job instead of just in simulation, and finding clever ways to train models on massive context windows without melting your GPU. Both are about making AI systems work better in the real world—whether that's a robot arm or your next coding assistant.

This Week's Papers

1. Teaching Robots to Learn from Their Mistakes in Real-Time

A new approach teaches robots to both think through actions before trying them and update their decision-making after failures, turning deployment into a learning experience rather than endless trial-and-error.

2. How We Trained an AI on 5 Million Tokens by Chopping Up Attention Heads

A new technique called UPipe lets you train language models on sequences 25% longer than before by processing attention heads in smaller chunks, using 87% less memory without slowing down.

That's a wrap for this week. Hit reply if any of these sparked an idea.

— Santthosh

Teaching AI to Think Out Loud Without the Rambling

Teaching AI to Think Less and Say More TL;DR — Researchers found that AI reasoning models ramble too much, and simply asking them to "be concise" then training them to do it naturally cuts their thinking by half while making them more accurate. What It Is When you

Teaching AI to Search Like a Pro: How Reinforcement Learning Created a Next-Gen Enterprise Search Agent

Teaching AI Agents to Search Like Experts (Without Needing Human Labels) TL;DR — Databricks trained an AI agent that's better at searching through company documents and answering complex questions than GPT-5 or Claude, using fake data generated by other AI agents plus reinforcement learning. What It Is Most

Distilled Weekly — Mar 02 - Mar 08, 2026

This week we're diving deep into making AI agents actually useful — and that means teaching them to remember what they've learned, know their limits, and verify their own work. We've got fascinating papers on everything from giving agents memory systems that work like notebooks

Can AI Agents Create Harder Math Problems By Writing Code?

Teaching AI to Write Its Own Math Homework (And Make It Harder) TL;DR — Researchers built a system where AI coding agents take existing math problems and automatically generate harder versions that are still solvable, potentially solving the shortage of challenging problems needed to train advanced math AI. What It