Part 4/7:
Sahil Chow provided insight into the data generation aspect of Reflection 70B, highlighting a successful approach involving the creation of a synthetic dataset. Starting with 10,000 samples and eventually scaling up to 100,000, the data aimed to challenge the model to reason through tasks that required mathematical computation, general reasoning, and multi-turn authentication processes.
Chow emphasized that the intention behind the generation of this data wasn't merely to enhance reasoning capabilities but also to train the model to acknowledge its mistakes. The training aimed at classifying problems based on difficulty, thereby only employing reflection in complex queries, thus avoiding the issue of overthinking in simpler scenarios.