Part 2/4:

The MIT researchers investigated the effectiveness of a technique called "test-time training," which involves temporarily updating the model parameters during inference using a loss derived from the input data. This approach allowed them to significantly improve the performance of LLMs on the ARC Benchmark, surpassing human-level reasoning for the first time.

The key to their success was a multi-step process that involved transforming the input data (e.g., flipping the image vertically or horizontally) and then using a hierarchical voting method to aggregate the predictions from these transformed inputs. This resembles a search for agreement or consistency across the outputs, ensuring that the chosen answer is the one that appears most frequently across the variations.

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