GLM 5.3: Scaling with post-training, intuitively explained

This post details the technical advancements in improving the GLM-5.2 model to GLM-5.3 through scaled post-training compute, environment design, and infrastructure engineering. It elaborates on the application of RL algorithms (SAO with compaction) and the slime framework (SGLang and Megatron) for efficient training and inference. Specific architectural optimizations discussed include Multi-head Latent Attention (MLA) for KV cache compression, DeepSeek Sparse Attention (DSA) with IndexShare for efficient attention computation at long sequence lengths, and Multi-token Prediction (MTP) for speculative decoding. The environment design focuses on realistic, agent-first task generation with robust verifier mechanisms to prevent reward hacking. The post also introduces Multi-teacher On-policy Distillation (OPD) as a new technique within the slime framework for GLM 5.3.