Coxon says Anthropic can retrain Mythos with less compute
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Former OpenAI and Anthropic researcher Jacob Coxon says Anthropic can now retrain Mythos with a small fraction of the compute originally required for training. On this basis, Coxon argues that Mythos is a point on a super-exponential algorithmic progress curve rather than a one-off leap. This claim comes from Coxon himself; the report does not provide the specific proportion of retraining compute or verification details.
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- The DecoderAnthropic can already retrain Mythos with "a fraction of the compute originally needed," says former researcher Coxon
Former OpenAI and Anthropic researcher Jacob Coxon says a slide from Anthropic's IPO roadshow was a major reason he left the company after about four months; the chart shows pre-training compute multiplier for code over time on a logarithmic y-axis, and Coxon says its steep climb points to accelerating algorithmic efficiency. He argues Anthropic's Mythos model was "not a one-time deviation, but a point on a superexponential," and that Anthropic could already retrain Mythos with a fraction of the compute originally needed. Coxon says algorithmic improvements are "on the verge of being automated" and wants frontier model development paused next year so competitors can catch up.
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