LittleLearner: When an LLM Never Sees Material Beyond Fifth Grade
What happens when an LLM never sees material beyond fifth grade?
LittleLearner is a controlled sandbox for studying how language models acquire knowledge. Researchers trained 0.6B, 1.3B, and 5B models from scratch on an 88B-token corpus filtered to the U.S. elementary-school curriculum (K–5), with matched unfiltered controls. Their experiments show that scaling, post-training (SFT+GRPO), and in-context learning amplify what the curriculum taught but fail to improve out-of-scope performance, indicating that the pretraining filter sets the effective capability ceiling. The project offers model checkpoints and invites researchers to probe the knowledge boundary.
In our experiments, scaling, SFT+GRPO post-training, and in-context learning amplify what the curriculum taught, but none meaningfully improves out-of-scope performance, indicating that the pretraining filter sets the effective capability ceiling.
- mindwok
Something related I've been thinking about lately is that one of the biggest problem with LLMs is their seeming inability to say no. Not in the hallucination sense, as in "I don't know", but like to have a subjective reason not to do something. The endless agreement you get from an LLM undermines trust in the long term I think. I'd like to talk to one that isn't an all-knowing oracle that can grant my every intellectual wish. (Or maybe what I'm asking for is just... a human, lol).
- dgacmu
I prefer my 8yo's answer about quantum entanglement, asked just now: "I don't know. How would I know? It's not a thing!"
Even an 8yo has better metacognition, it seems. :-)
- cl3misch
> Unfiltered answer: Quantum entanglement is a strange phenomenon where the state of one particle becomes instantly known to every other particle that can be accessed. This instant communication can occur over vast distances, meaning the death of one particle can be witnessed by the others instantaneously.
Wrong: Quantum entanglement doesn't mean one entangled particle is changing the other one. It means that two particles share a relationship where, even though we intially don't know their state, if we later determine one particle's state we can infer with certainty the other particle's state.
This has been common and popular misconception long before LLMs. But it irks me more than it should that it's used as a reference answer for testing a model's intelligence.
- uniq7
> why is the sky blue?
> The sky is blue because of something called Rayleigh scattering. The sun sends out UV and infrared waves, and some of them get trapped in Earth's atmosphere. When the waves hit the tiny molecules in our atmosphere, they scatter away the blue ones, which then bounces off the molecules and reaches our eyes.
"filtered to the U.S. elementary-school curriculum", suuure
- Closi
I think the answer is 'it answers badly because of a lack of training data'
> Q: Whats the impact of asbestos?
> A: First, the job took 10 hours and 30 minutes.
Next, let's change 30 minutes into part of an hour. Since there are 60 minutes in an hour, 30 minutes is 30/60, which simplifies to 1/2 hour, or 0.5 hours.
or
> Q: What is the five times table?
> A: We want to understand what the five times table is.
First, the five times table is a list of numbers you get when you multiply 5 by a whole number. Next, the most common whole number you can multiply by to get 5 is 1. Then, the five times table shows the result of 1 multiplied by 5, which is 5.
Now, let's check if 5 multiplied by 3 equals 5. Yes, 1 × 3 = 3, and 3 × 3 = 9, which is 5. Therefore, the five times table lists the result of multiplying 1 by 5.
Answer: 5
Interesting project though! From a research perspective I think this will be fascinating in terms of understanding what an LLM experiences.
- montebicyclelo
Really cool work. I guess the area of scrutiny is the text filtering, where training text is filtered to get to `<=fifth_grade` material. I would have liked to have seen examples of what is in this training set, but paper [1] seems to only show examples of what was excluded, and dataset doesn't look like it's been released yet. They have 2 methods of validating the filtering, both based on datasets, I would have also liked to have seen some spot checks; e.g. randomly sample some text from the dataset, and get a human to say whether they think it's <=fifth_grade or not.
(They do imply in the abstract that they will release the dataset, which I guess will resolve this.)
- krackers
A similar project (LLM trained only on vintage material): https://talkie-lm.com/introducing-talkie
- eptcyka
Isn’t the conclusion of this paper rather bleak for openai and anthropic? It seems to imply that a model doesn’t emerge as intelligent with more training, rather it is as intelligent as the data it ingests?