Fine-tuning experiments on CoT controllability

We find that a small amount of fine-tuning on instruction following in the CoT generalizes to meaningful increases in CoT controllability on an out-of-distribution set of tasks. We fine-tune four reasoning models on small datasets of instruction-following reasoning data and OOD controllability rises from an average of 2.9% to 8.8% across four models.