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Frontier intelligence at a fraction of the cost. Here AI models GLM 5.3: Scaling with post-training, intuitively explained GLM 5.3's gains came entirely from post-training. An intuitive look at the environment design, RL algorithms, and infrastructure behind the leap. Authors Chloe Florit Alex Ker Last updated August 28, 2026 Share Sutton's bitter lesson said general-purpose methods that scale with compute will win. GLM-5.3 is a perfect application of this law to RL, and it means a model can leapfrog its predecessor without a new pre-training run: an identical base model (GLM-5.2) improved over 50% through scaled post-training compute, deliberate environment design, and efficient infrastructure engineering. In this post, we'll look at how environment design led to GLM-5.3. Then we'll look at how the architecture behind GLM 5.2 carried over to enable efficient training and inference possible. 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