Adjusting the step size of learning models allows for more careful exploration of deeper insights without overshooting potential breakthroughs. While smaller models like LAMA38B benefit from refined training techniques, larger models such as the 405 billion parameter Behemoth show negligible improvements, indicating their strong in-context learning capabilities. This suggests that the sheer size of a model can overshadow the need for fine-tuning, as generality can dominate specificity. Ultimately, achieving scale enhances performance but does not completely resolve all challenges in model training.
Our 176th episode with a summary and discussion of last week's big AI news!
NOTE: apologies for this episode coming out about a week late, things got in the way of editing it...
With hosts Andrey Kurenkov (https://twitter.com/andrey_kurenkov) and Jeremie Harris (https://twitter.com/jeremiecharris)
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