https://arxiv.org/abs/2411.07279

TTT significantly improves performance on ARC tasks, achieving up to 6× improvement in accuracy compared to base fine-tuned models; applying TTT to an 8B-parameter language model, we achieve 53% accuracy on the ARC’s public validation set, improving the state-of-the-art by nearly 25% for public and purely neural approaches. By ensembling our method with recent program generation approaches, we get SoTA public validation accuracy of 61.9%, matching the average human score.

Test-time training (TTT) enables parametric models to adapt during inference through dynamic parameter updates, an approach that remains relatively unexplored in the era of large language models. This technique is a form of transductive learning, where models leverages the test data structure to improve its predictions.

I made a version of this market which allows for closed source LLMs: https://manifold.markets/RyanGreenblatt/by-when-will-85-be-reached-on-the-p

This is your chance to win free mana betting against SG, which is a guaranteed winning strategy exploited by top traders such as jackson

Now James is on the action. Thanks!

Note that this prize doesn't allow for close source models to be used in doing the actual task.

Of course, distillation is possible etc.

Did you mean “by 2025” or “in 2025”? Meaning, how would it resolve it the prize were claimed in 2024?

You're right, thanks. Correcting.

@mckiev i might take you up on the offer, but what's your reasoning for 85% accuracy? we're at 30% right now

Vibes based for the most part ;)

Tasks seem easy, 100s of billions being invested in ai right now, and there is hype and status around beating this benchmark