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Recent research from Google DeepMind suggests that training language models on synthetic data generated by smaller, weaker models can outperform those trained on data from larger, stronger models. This finding challenges the current practices in the field of language model training, which often rely on high-quality synthetic data from strong models. The study highlights a potentially compute-optimal approach for enhancing the reasoning capabilities of language models, which could lead to more efficient use of computational resources.
Can Smaller AI Models Outperform Giants? This AI Paper from Google DeepMind Unveils the Power of ‘Smaller, Weaker, Yet Better’ Training for LLM Reasoners The researchers from Google DeepMind introduce a novel approach that challenges the reliance on SE models for synthetic data… https://t.co/8149XDsYiH
Weaker is Better - Great paper from @GoogleDeepMind **Problem**🔍: Training language models (LMs) on high-quality synthetic data from strong LMs is common for improving reasoning, but may not be compute-optimal under fixed inference budgets. **Key Insights from this Paper… https://t.co/EmoRHzJpw9
Microsoft Researchers Combine Small and Large Language Models for Faster, More ... https://t.co/N7vKyXsXkk #ResponsibleAI
