GLM-5 Technical Report (arxiv.org)

The recent technical report on GLM-5 introduces a groundbreaking foundation model that transitions from "vibe coding" to "agentic engineering," enhancing capabilities in reasoning and coding. This next-generation model employs Dynamic Sparse Activations (DSA), which notably reduces training and inference costs while preserving long-context fidelity. Additionally, it features a new asynchronous reinforcement learning (RL) infrastructure that improves post-training efficiency by separating generation from training, along with innovative asynchronous RL algorithms that excel in complex, long-horizon interactions. Significantly, GLM-5 sets a new benchmark in real-world coding tasks, outpacing previous models in end-to-end software engineering challenges. These advancements suggest a shift in how AI models can be trained and deployed for practical applications, potentially enhancing their autonomy and alignment with user needs. This leap in performance on major open benchmarks marks a pivotal moment in AI/ML, indicating that future models may increasingly handle complex coding environments with unprecedented efficiency and scalability.