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Bold AI Predictions From Cohere Co-founder
Machine Learning Street Talk (MLST)
Harnessing Synthetic Data for Enhanced Reasoning
Reasoning capabilities can be significantly improved through the combination of base models and reinforcement learning from human feedback (RLHF). By generating synthetic datasets, models can learn to reason from first principles, creating a self-reinforcing loop of knowledge and understanding. Synthetic data plays a critical role not just in self-teaching but also in expanding the distribution of data relevant to specific tasks, allowing models to generate additional data where real datasets are scarce. This strategic use of synthetic data requires an initial foundation of real data to guide the model, which can then be tuned to produce increasingly diverse and useful datasets for improving model reasoning.
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