Modern language models are all the rage, writing poetry and coding like they’ve got something to prove. But let’s cut through the noise—these models don’t learn from experience. They’re parrots, not prodigies. MIT researchers are trying to change that by teaching these models to tweak their own parameters when they get new info. Sounds promising, but let’s not pop the champagne just yet.
This project, called Self Adapting Language Models (SEAL), aims to make AI learn continually. In theory, this could give us chatbots that actually evolve with user preferences. But here’s what this really means: we’re trying to teach machines to mimic human learning. We’ve been chasing this goal since AI was a glimmer in a scientist’s eye.
SEAL works by having models generate their own training data based on the input they receive. The idea is to see if a model’s output can be used as its own teacher. It’s a nice thought, but remember, we’re still dealing with machines that can forget everything they’ve learned in the blink of an update.
The researchers tested SEAL on smaller versions of Meta’s Llama and Alibaba’s Qwen. They say it could work with larger models, but let’s not kid ourselves—scaling up is never as simple as it sounds. These tests showed SEAL helps models keep learning beyond their initial training, which is great, but don’t expect miracles overnight.
MIT’s Pulkit Agrawal says SEAL touches on key AI themes, like self-directed learning. But here’s the catch—these models still suffer from “catastrophic forgetting.” That’s a fancy way of saying they lose old knowledge when they learn something new. It’s a major roadblock, and it highlights the gap between artificial and biological learning.
SEAL is also a computational beast. It’s not clear how to schedule new learning periods effectively. One researcher even suggested letting models “sleep” to consolidate new info. Cute idea, but we’re a long way from AI needing a nap.
Despite its flaws, SEAL is a step forward. It’s not a magic bullet, but it’s a new path for AI research. Just don’t get swept up in the hype—AI’s got a long road ahead before it can truly learn like us.
