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NanoGPT Slowrun: 10x Data Efficiency with Infinite Compute
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Posted by sdpmas. Score: 89 points. Comments: 14.
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- Praise: Data efficiency is crucial as the gap between compute and data continues to widen, making advancements like NanoGPT's approach significant for future AI development.
- Praise: The potential for LLMs to bootstrap and improve themselves in a learning loop could revolutionize AI training methodologies, leading to more autonomous systems.
- Concern: The claim of 10x data efficiency is questionable, as many labs are generating higher quality artificial data with increased compute, challenging the relevance of this metric.
- Concern: Comparing to Chinchilla-optimal training is misleading, as the industry has moved beyond those benchmarks, with small models now trained on significantly larger datasets.