@omarsar0: GLM-5.2 is great at design (Opus level IMO). I am also starting to see great results with long-running tasks, too. How …

X AI KOLs Following Models
glm-5.2 ai-model reward-hacking anti-hacking long-running-tasks open-weight design

Summary

GLM-5.2, an open-weight model with Opus-level design capabilities, incorporates an anti-hacking module trained via RL to mitigate reward hacking and improve performance on long-running tasks.

GLM-5.2 is great at design (Opus level IMO). I am also starting to see great results with long-running tasks, too. How is this possible? I think there are a few clever hacks. But I just came across this from the official blog, and they actually trained this model with an anti-hacking module. RL, as many know, comes with this issue of reward hacking that often enables the model to take weird and suboptimal shortcuts. Not only that, but it makes the models sometimes feel like it's sometimes "lazy" or just plain "dumb" at times, including other issues like intent misalignment, verbosity, sycophancy, deception, etc. And you really don't want that for long-running tasks operated by coding agents. This is a great insight. If you use the standard /goal (in 5.5 or 4.8), you notice the models often take shortcuts that lead to long-running tasks (wasting tokens along the way) but with poor results. This is why I advocate for a focus on better verifiers. So this anti-hacking idea is a model capability that should, in theory, lead to better results on long-horizon tasks. I've seen efforts here and there in a few research papers, but haven't seen it translated to much, much less in a frontier, open-weight model. This might be contributing to some of the great results we are seeing with GLM-5.2, but I suspect there is more, of course, like better verification capabilities. It's not clear how all of these training signals lead to downstream capabilities, but this is something to look at closely with newer models.
Original Article
View Cached Full Text

Cached at: 06/22/26, 03:37 PM

GLM-5.2 is great at design (Opus level IMO).

I am also starting to see great results with long-running tasks, too.

How is this possible?

I think there are a few clever hacks. But I just came across this from the official blog, and they actually trained this model with an anti-hacking module.

RL, as many know, comes with this issue of reward hacking that often enables the model to take weird and suboptimal shortcuts. Not only that, but it makes the models sometimes feel like it’s sometimes “lazy” or just plain “dumb” at times, including other issues like intent misalignment, verbosity, sycophancy, deception, etc. And you really don’t want that for long-running tasks operated by coding agents.

This is a great insight. If you use the standard /goal (in 5.5 or 4.8), you notice the models often take shortcuts that lead to long-running tasks (wasting tokens along the way) but with poor results. This is why I advocate for a focus on better verifiers.

So this anti-hacking idea is a model capability that should, in theory, lead to better results on long-horizon tasks.

I’ve seen efforts here and there in a few research papers, but haven’t seen it translated to much, much less in a frontier, open-weight model.

This might be contributing to some of the great results we are seeing with GLM-5.2, but I suspect there is more, of course, like better verification capabilities. It’s not clear how all of these training signals lead to downstream capabilities, but this is something to look at closely with newer models.

Similar Articles

GLM 5.2 vs. Opus

Hacker News Top

GLM 5.2 is a new open-weights model from Z.ai, compared against Claude Opus in a 3D game coding task. Opus performed faster and cleaner, but GLM 5.2 offers compelling cost and accessibility advantages.

GLM-5.2: Built for Long-Horizon Tasks

Hugging Face Blog

Z.AI introduces GLM-5.2, a flagship model designed for long-horizon tasks with a solid 1M-token context, improved coding capabilities, and an MIT open-source license, showing competitive performance against leading models like Opus 4.8 and GPT-5.5.