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!
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.
Why it matters
Names the mechanisms behind GLM-5's software engineering results: DSA to cut long-context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition → training and inferenceRunning a trained model to get answers — the phase where AI is actually used, as opposed to trained.Full definition → cost, asynchronous RL infrastructure, and AI agentAn 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.Full definition → RL over long-horizon trajectories.
