Another important use of synthetic data for safety purposes could be to trade off Chain of Thought (CoT) length complexity for e.g. architecture depth complexity, by creating synthetic CoT data, as suggested by Auto-Regressive Next-Token Predictors are Universal Learners. This could allow for differentially more transparent systems, with relatively weaker forward passes, while still being Turing-complete.
People have already been training models on doing CoT and similar techniques, certainly via fine-tuning, and I strongly suspect also at the “significant proportion of synthetic data in the training dataset” level. My impression (from the outside) is that it’s working well.
Another important use of synthetic data for safety purposes could be to trade off Chain of Thought (CoT) length complexity for e.g. architecture depth complexity, by creating synthetic CoT data, as suggested by Auto-Regressive Next-Token Predictors are Universal Learners. This could allow for differentially more transparent systems, with relatively weaker forward passes, while still being Turing-complete.
People have already been training models on doing CoT and similar techniques, certainly via fine-tuning, and I strongly suspect also at the “significant proportion of synthetic data in the training dataset” level. My impression (from the outside) is that it’s working well.