前Anthropic研究员Coxon:Anthropic已能用"一小部分原始计算资源"重新训练Mythos模型
Anthropic can already retrain Mythos with "a fraction of the compute originally needed," says former researcher Coxon
前Anthropic和OpenAI研究员Jacob Coxon称,Anthropic的IPO路演幻灯片是他离职的主要原因,该幻灯片显示其Mythos模型在春季实现重大能力跃升后,已能用"一小部分原始计算资源"进行重新训练,这表明算法效率正加速提升。
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前Anthropic研究员Coxon:Anthropic已能用"一小部分原始计算资源"重新训练Mythos模型
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Jacob Coxon, former OpenAI and Anthropic researcher who brought AI fears into the mainstream, says a slide from Anthropic's IPO roadshow was a major reason he quit.
Coxon says a slide Anthropic released for its IPO roadshow was a key reason he left the company after only about four months. The chart shows how much compute Anthropic needs over time to train its models, and the axis is compressed so that a 10x and a 100x increase barely look different.
Yet the curve still climbs steeply, which Coxon says shows that algorithmic efficiency is improving not just quickly but at an accelerating rate. "It's an insane plot and I'm surprised they made it public," he writes.
Coxon argues that Anthropic's Mythos model, a major capability jump in the spring, was part of this accelerating trend. "Mythos was not a one-time deviation, but a point on a superexponential. They could already retrain Mythos with a fraction of the compute originally needed," he writes.

He says algorithmic improvements are "on the verge of being automated," which is why he wants frontier model development paused next year so competitors can catch up. A pause, he writes, would "commoditize human-level intelligence. It would only harm OpenAI and Anthropic."
Coxon previously trained AI models at OpenAI and, most recently, Anthropic. He drew wide attention in September with public warnings about the risks of advanced AI systems, echoed by other protagonists in the industry. One of their fears is that advanced, self-improving AI could get out of control.
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来源: The Decoder · the-decoder.com