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智谱唐杰：万亿参数是行业早期的探索

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TL;DR - Zhipu AI co-founder Tang Jie argues that scaling laws should optimize parameters, data, inference cost, compute depth, and post-training together—not prioritize trillion-parameter models. GLM-5.3 illustrates this approach by retaining GLM-5.2’s base model and parameter counts while improving performance through expanded long-horizon environments and reinforcement learning.

  • Tang calls the industry’s early pursuit of trillion-parameter models a detour, citing Chinchilla’s evidence for balancing model size with training data.
  • Lifecycle inference costs favor smaller models trained on more tokens, while MoE architectures require distinguishing total parameters from activated parameters and effective compute depth.
  • Total parameters mainly expand knowledge capacity; activated compute and depth are more important for sustained multi-step reasoning.
  • Zhipu spent one month scaling GLM-5.3’s long-horizon task environments and reinforcement learning, while leaving architecture and parameter counts unchanged.

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智谱唐杰：万亿参数是行业早期的探索

WeChat: 智东西 2026-08-19
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Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-20 14:14:59.017040 UTC

TL;DR - Zhipu AI co-founder Tang Jie argues that scaling laws should optimize parameters, data, inference cost, compute depth, and post-training together—not prioritize trillion-parameter models. GLM-5.3 illustrates this approach by retaining GLM-5.2’s base model and parameter counts while improving performance through expanded long-horizon environments and reinforcement learning.

  • Tang calls the industry’s early pursuit of trillion-parameter models a detour, citing Chinchilla’s evidence for balancing model size with training data.
  • Lifecycle inference costs favor smaller models trained on more tokens, while MoE architectures require distinguishing total parameters from activated parameters and effective compute depth.
  • Total parameters mainly expand knowledge capacity; activated compute and depth are more important for sustained multi-step reasoning.
  • Zhipu spent one month scaling GLM-5.3’s long-horizon task environments and reinforcement learning, while leaving architecture and parameter counts unchanged.
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