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Retaining more copies of low-frequency content while aggressively pruning high-frequency duplicates can significantly boost model performance during pretraining.
AI can now design better AI: ASI-Evolve discovers SOTA architectures, curates pretraining data, and designs RL algorithms, outperforming human-designed baselines by significant margins.
Pretraining isn't just about scaling data volume; daVinci-LLM's ablations reveal that data processing depth, domain-specific strategies, and compositional balance are equally critical for unlocking LLM capabilities.
