The GLM-5 technical report: DSA, asynchronous RL, and agent RL
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Presenting the GLM-5 Technical Report! https://t.co/CGjxEISvFK After the launch of GLM-5, we’re pulling back the curtain on how it was built. Key innovations include: - DSA Adoption: Significantly reduces training and inference costs while preserving long-context fidelity - Asynchronous RL Infrastructure: Drastically improves post-training efficiency by decoupling generation from training - Agent RL Algorithms: Enables the model to learn from complex, long-horizon interactions more effectively Through these innovations, GLM-5 achieves SOTA performance among open-source models, with particularly strong results in real-world software engineering tasks.
For those who want to dive deeper into the technical details, you can find our paper and join the discussion on Hugging Face here: https://t.co/jLS6FE9a9d Looking forward to hearing all your thoughts!
Context
Z.ai's GLM-5 report explains how it keeps training moving when AI agents take different amounts of time to finish tasks. In conventional synchronized training, long-running attempts can leave computing resources waiting. The report's asynchronous-training section separates the systems generating task attempts from those updating the model.
Completed attempts accumulate into training batches while generation continues. Updated model weights are periodically sent back to the generation system. This reduces dependence on everyone finishing together, but also means attempts can come from different model versions; the authors describe filtering overly stale examples to manage that complication.
- inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
- context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
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