Part 2/4:
The researchers identified three crucial components for successful test time training:
Initial Fine-Tuning on Similar Tasks: The model must be capable of performing well on related tasks before the test time training can be effective.
Auxiliary Task Format and Augmentations: The researchers generate diverse training data by applying geometric transformations to the test input, creating variations that the model can learn from during the test time fine-tuning process.
Per-Instance Training: The model updates its parameters for each test input, effectively creating a specialized prediction model for each instance.
Impressive Results on the ARC Benchmark
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