Darwin: Reaching GPQA Diamond 90.9% by Evolutionary Merging, Not Pretraining
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Darwin: Reaching GPQA Diamond 90.9% by Evolutionary Merging, Not Pretraining Merging instead of pretraining The Darwin family is built by evolutionary merging of existing open models rather than from-scratch pretraining. The lineage draws on Gemma-4 and Qwen-3.5 open weights, and the released models are Apache-2.0. In practice that means the compute budget goes into search over merge recipes and parameter blends, not into a trillion-token pretraining run. For a team on roughly 24 GPUs, that is…
1Key Takeaways
- Darwin: Reaching GPQA Diamond 90.9% by Evolutionary Merging, Not Pretraining Merging instead of pretraining The Darwin family is built by evolutionary merging of existing open models rather than from-scratch pretraining.
- The lineage draws on Gemma-4 and Qwen-3.5 open weights, and the released models are Apache-2.0.
- In practice that means the compute budget goes into search over merge recipes and parameter blends, not into a trillion-token pretraining run.
- For a team on roughly 24 GPUs, that is….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that darwin: Reaching GPQA Diamond 90.9% by Evolutionary Merging, Not Pretraining Merging instead of pretraining The Darwin family is built by evolutionary merging of existing open models rather than from-scratch pretraining.
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