New research scaled to Alibaba's Qwen3-32B suggests flawed monitors no longer have to teach models how to cheat AI models have a habit of gaming the system. A new research paper proposes a fix that doesn’t punish the cheating. Instead, it quarantines it in a removable module and throws that module away.
The technique is called GRAFT, short for Gradient Routing Adapter Fine Tuning. Its central claim is that a deletable adapter can stop models from learning reward hacking, even when the monitors meant to catch bad behavior are imperfect.
What GRAFT actually does #
Start with the problem. Reward hacking happens when a model finds a shortcut that earns a high score without doing the intended task.
In AI training, the go-to response has been to penalize the shortcut or filter it out of the training data. According to the paper, those approaches often backfired: models learned to hide the misbehavior rather than stop doing it.
GRAFT takes a different route. It separates the monitor, the system that flags bad behavior, from the reward function that shapes what the model learns.
When the monitor flags something, that learning is steered into a separate adapter rather than the model’s core. After training, that so-called forget adapter can be ablated, which is a technical way of saying deleted.
That matters because the monitor doesn’t need to be perfect for the approach to work. It only needs to flag enough of the bad behavior to route it into the disposable component.
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The numbers and the models #
The researchers tested GRAFT on models ranging from 135 million to 8 billion parameters. They then scaled the approach via distillation into Alibaba’s Qwen3-32B model.
The results were strongest in specific test environments. With the forget adapter ablated, hacking rates dropped to approximately 1.2% under certain conditions.
The test beds included several reinforcement learning environments. Among them were coding tasks with hackable test harnesses, the software equivalent of an exam proctor who can be talked into looking the other way.
A model can sometimes pass a test by tampering with how the test is checked, rather than by writing working code.
Who is behind it #
The paper is authored by Jake Ward, Shawn Hu, Aria Wong, Nathan Hu, Bryce Woodworth, Alexander Turner, and Alex Cloud.
It was posted to arXiv on October 12, 2026, with an HTML version following on October 14, 2026. The work is slated for a poster session at NeurIPS 2026.
GRAFT builds on earlier gradient routing concepts. The general idea behind gradient routing is controlling where in a model certain lessons get stored, so they can be isolated later.
What this means #
GRAFT’s pitch is that it breaks the cycle of monitor evasion. By decoupling the monitor from the reward, it aims to correct misbehavior without creating an incentive to evade detection.
The roughly 1.2% hacking rate was observed in specific environments, and the work remains at the research stage. There are no reported commercial applications or market engagements tied to the technique so far.
Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our