Seesaw Model
The seesaw model describes a training strategy for AI systems in which capabilities compete with each other like two sides of a seesaw: improving one side worsens the other. This tension is a central problem in building AI models that are meant to perform multiple tasks well at the same time.
When training an AI model — that is, the process by which a system learns from many examples — something undesirable often happens: making the model better at one skill makes it worse at another. This phenomenon is called the seesaw model, after the playground equipment where one side goes up when the other goes down. The model cannot keep both sides up at the same time. This may sound like a minor technical issue at first, but it is actually one of the most persistent conflicts in developing modern AI systems.
The price of a better model
The seesaw problem arises as soon as a model is supposed to master several different tasks at once. Should the same model state facts precisely while still sounding fluent and natural? Should it give short answers but also be able to structure long texts? Each of these requirements pulls the training in a different direction.
In practice, this is especially evident in so-called benchmark tests — standardized tasks researchers use to compare AI models. A model that is specifically trained on one benchmark performs better there, but often loses points on another. Developers of AI systems therefore face a constant trade-off: what do they sacrifice in order to gain somewhere else?
Why the seesaw can’t simply be locked in place
The reason for seesaw behavior lies in the training process itself. A model learns by making mistakes and being corrected. Every correction changes the model’s entire internal web — millions or even billions of small settings known as parameters. An adjustment that favors task A can shift the parameters in a way that makes task B perform worse. There is no separate compartment for each skill; everything is interconnected.
Researchers try to work around this problem using various methods. One approach is to mix the training data very carefully so that no single task crowds out the others. Another is to split the model’s development into several phases: first broad foundational training, then targeted fine-tuning — that is, refinement for specific tasks. But even these approaches don’t fully solve the seesaw problem, they only mitigate it. A related problem arises especially during fine-tuning, known as forgetting: the model loses older abilities while learning new content.
Another approach comes from architecture: Mixture-of-Experts models — systems that activate only certain subcomponents for each request — could help separate capabilities more cleanly. Whether this stabilizes the seesaw permanently is still a subject of ongoing research.
Seesaw effects in real products and debates
The seesaw model is not a purely theoretical concept — it regularly appears in coverage of major AI systems. When a company releases a new version of its chatbot and users notice that it has gotten worse at certain tasks, a seesaw effect is often behind it. OpenAI experienced this with various GPT versions, where users reported performance declines in certain areas after updates.
The phenomenon also plays a role in the competition between AI companies. When a model sets a record on a benchmark, experts immediately ask: which other capability was sacrificed to achieve it? A top result on a single test says little about how balanced a model is overall. The seesaw model explains why it is so difficult to lead in all disciplines at the same time.
For users, this means: no model is equally good at everything. Which capabilities a provider has prioritized depends on decisions made during training — and these decisions are always trade-offs.