GLM (Z.AI)

Sources#

Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning

What it is#

The GLM (General Language Model) family from Z.AI (Zhipu AI), a lab spun out of Tsinghua University's KEG group. In this corpus GLM appears as the large-MoE open-weight line — the axis of open weights that competes on frontier capability rather than on edge efficiency, the counterpart to Gemma 4's smaller-and-cheaper strategy. Three members appear in the SAO paper:

Model	What's known here
GLM-4.5	"Agentic, reasoning, and coding (ARC) foundation models" (Team GLM, arXiv 2508.06471, 2025). The generation the SAO authors' techniques descend from.
GLM-4.7	A frontier-competitive reasoner. In Table 1 it beats GPT-5 High and Claude-Sonnet-4.5 on AIME2025 (95.7), HMMT Nov-2025 (93.5) and IMOAnswerBench (82.0). Also used as the LLM judge for reward assignment in SAO's online-learning simulation.
GLM-5.2	A 750B-total / 40B-active open MoE — the production model SAO was built to train. The paper's framing: "successfully deployed in the agentic RL pipeline for training the open GLM-5.2 model."

Why it's in the wiki#

Two reasons, both connecting to existing threads.

It is the reason SAO exists. Asynchronous single-rollout RL is not an academic exercise here — it is the training method behind a shipped 750B-A40B open model. That production heft is what separates this paper from a methods note: the stability results (~1000 stable steps vs GRPO's collapse at ~160) had to hold at GLM-5.2 scale.

It is a data point for the open-weight frontier. The Open-Weight Frontier Gap observes that open weights at the frontier means 744B–1.6T MoEs. GLM-5.2 at 750B-A40B is exactly that class. And GLM-4.7's Table 1 numbers — ahead of two closed frontier models on three of four math-reasoning benchmarks — are a concrete instance of an open model reaching the closed frontier on a capability axis, which is the gap that page tracks. Read the two together: Gemma competes at 31B on efficiency and sits at Arena rank 43; GLM competes at 750B on capability and lands among the frontier reasoners. Same "open-weight" label, opposite strategies.

The Tsinghua / Z.AI authorship thread#

SAO's authors are Zhenyu Hou, Yujiang Li, Jie Tang, Yuxiao Dong (Tsinghua), with ZH and YL noting internships at Z.AI. Zhenyu Hou also appears on the GLM-4.5 author list, and Tang and Dong are the Tsinghua faculty behind the long-running GLM/ChatGLM line — so the paper is effectively the GLM team documenting the RL infrastructure behind their own model, published academically. Treat the GLM-4.7-beats-GPT-5 numbers with that in mind: they are empirical (measured benchmark results) but first-party to the same lab whose method the paper is selling.

Connections#

Single-Rollout Optimization — SAO, the RL method deployed to train GLM-5.2; GLM-4.7 is both its benchmark ceiling and its online-sim judge
Asynchronous RL for LLMs — the training-loop regime GLM-5.2 was trained under
The Open-Weight Frontier Gap — GLM-5.2 is the 744B–1.6T-class open MoE that page describes; GLM-4.7's numbers are the capability-side counterexample to Gemma's efficiency-side positioning
Gemma 4 — the sibling open-weight family with the opposite strategy (small + efficient vs large + frontier-capable)
LLM-as-a-Judge — GLM-4.7 serves as the reward judge in the online-learning experiment

Sources#

Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning — GLM-5.2 as deployment target (Abstract, §1); GLM-4.7 in Table 1 and as online-sim judge (§4.5); GLM-4.5 as the referenced foundation model (Team GLM, arXiv 2508.06471). arXiv 2607.07508, 2026-07-08. empirical, first-party to the GLM lab.