A survey by Epoch AI of some of these techniques, like scaffolding, tool use, and so on, finds that techniques like this can typically result in effective compute gains of 5-30x on many benchmarks. METR (an organization that evaluates models) similarly found very large performance improvements on their set of agentic tasks, via unhobbling from the same GPT-4 base model: from 5% with just the base model, to 20% with the GPT-4 as posttrained on release, to nearly 40% today from better posttraining, tools, and agent scaffolding.
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