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Robots That Learn From Their Mistakes (Instead of Repeating Them) TL;DR — 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. What It Is When you give a robot an
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Welcome to this week's Distilled! We're diving into two papers that tackle very different but equally critical problems in AI development. First up: a surprisingly tiny fraction of training tokens can derail your entire LLM, and second, we're looking at how GLM-5 is pushing
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Why Your LLM Goes Crazy During Training (And How 0.01% of Tokens Are to Blame) TL;DR — When training language models with reinforcement learning, about 1 in 10,000 tokens get wildly oversized updates that destabilize everything. Masking just these troublemakers fixes the problem. What It Is Researchers at
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GLM-5: Teaching AI to Actually Build Software, Not Just Write Code Snippets TL;DR — GLM-5 shifts from helping you write code to actually acting as a software engineer that can handle complex, multi-hour development tasks autonomously. What It Is The team at Zhipu AI built GLM-5 to solve a problem