Mirroring the way humans learn

“It’s important to understand that in these projects we are primarily collecting data to train AI systems using a method called reinforcement learning,” said Oleg. 

Reinforcement learning, or RL, is a way of training an AI through trial and error. Imagine it as practice with feedback: the model attempts a task, gets evaluated, and gradually improves based on what worked and what didn’t.

Evaluation often combines the power of automation and human knowledge, using both automatic evaluation rules and rubrics that AI Trainers follow. Rubrics provide a clear checklist of criteria that describes what a “good” answer looks like and what should count as correct or acceptable. Reinforcement learning is a fine balance between pushing the model while ensuring it gets it right — at least some of the time. 

“The  goal is not to break the model completely. Tasks need to be challenging enough that the model often makes mistakes, but not so difficult that it never succeeds,” explained Oleg. This approach mirrors how humans learn. That “aha” moment often comes when the materials and lessons match the learner’s current level but are just slightly more difficult than what they already know. 

“It requires carefully balancing task difficulty so that the model struggles but can still improve. In practice, this balance is found through repeated cycles of adjusting the task and reviewing how the model performs at each step,” said Oleg. 

What “breaking the model” looks like 

Adversarial testing, or stress-testing, is your chance to get creative and throw tricky prompts at the model to make it fail or crash in different ways. Some common mistakes the model might make include:

• Reasoning errors: These include breakdowns in logic. For example, miscalculating numbers or drawing the wrong conclusion from given information.

• Tone mismatches: These happen when the model uses the wrong style or emotional tone. For example, sounding casual when answering a serious question or switching tones mid-conversation.

• Contradictions: These occur when the model gives statements that conflict with each other. For example, saying something is both safe and unsafe in the same response.

• Missing context: These happen when the model answers without enough information. For example, giving a definitive answer without asking for location, time, or other necessary details.

• Overgeneralizations: These occur when the model makes broad claims that do not apply in all cases. For example, saying “everyone” or “always” when the situation depends on context.

These “failures” are actually successes! The goal of stress-testing is to outsmart or trick the model — which is often easier than it sounds. AI models have some level of previous training, meaning trainers need to find unique ways to bypass the hard work previous trainers contributed. 

Looking for practical tips? Dive into how our AI Trainers handled a tough red-teaming project that pushed models (and our trainers’ creativity) to the limits. 

What makes a good adversarial trainer?

It’s easy to assume that effective stress-testing simply requires deep subject-matter expertise. After all, who better to challenge an AI system on legal reasoning than a lawyer? But as Oleg explains, expertise on its own doesn’t guarantee useful training data — and in some cases, it can even be limiting.

“Real-world problems taken directly from professional experience either run out quickly or turn out not to be suitable for training AI. They often fail to meet the specific requirements needed for learning, such as being the right level of difficulty,” said Oleg. 

What matters more is the ability to analyze and iterate, or to improve a task step-by-step. Iterative thinking isn’t necessarily a talent you’re born with. It can be honed through simple, repetitive steps: 

• Create a task

• Observe how the model tries to solve it, where it succeeds or fails, and what kinds of mistakes it makes

• Adjust the task based on those observations

• Repeat the process

“This kind of iterative thinking — testing, observing, refining, and testing again — is central to the role,” stressed Oleg. Without this approach, stress testing quickly becomes shallow or repetitive, exposing only obvious failures while missing the subtler weaknesses that surface over time. Iteration is what turns isolated test cases into meaningful signals, and ultimately helps models improve in ways that reflect real-world use.

Beyond domain expertise and iterative thinking, Oleg explained that “it’s also important to have at least basic programming and code-reading skills, even if your main field has nothing to do with software development.” 

This doesn’t mean you need to be a professional programmer, but it’s useful to understand how models solve problems and perform tasks. For example, models tend to solve tasks using the tools available to them, like search engines and programming environments. Being able to read and roughly understand these model-generated solutions is crucial. 

“Without that understanding, it becomes much harder to see why the model failed or succeeded, and that insight is essential for moving forward in the iterative cycle,” said Oleg.