ME News report, September 9 (UTC+8): Dwarkesh Patel released an experimental analysis training representative model architectures and datasets from 2019 to 2025 under a maximum compute budget of 1e19 FLOPs. The findings show that data improvements yielded a 12.0x gain in compute efficiency, while model improvements contributed a 3.7x gain—meaning data accounted for approximately 3.24 times the impact of model advancements. 🔗 Read the original article via AIHOT · https://aihot.news/items/cmtsx0wm4041jrob5liwjypsq (Source: AiHot)
Dwarkesh Patel Study: Data Improvements Drive 3.24x Greater Compute Efficiency Than Model Advancements
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Summary
According to inflation data from MetaEra, Dwarkesh Patel released a study on September 9 (UTC+8) analyzing model and data improvements from 2019 to 2025 under a 1e19 FLOPs budget. Data improvements increased compute efficiency by 12.0x, while model changes contributed an additional 3.7x. On-chain data revealed that data upgrades were 3.24 times more effective than model changes.
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