When you fine-tune a model using Supervised Fine-Tuning (SFT), creating high-quality chain-of-thought (CoT) reasoning traces for your training data is often impractical and can be prohibitively expensive. As a result, you might choose to skip reasoning during SFT and train with only inputs and outputs. However, reasoning is a key capability of the Amazon Nova 2 family of models, and it’s been shown to significantly enhance prediction performance in Nova and other frontier models, often with performance gains on hard problems like coding and math. With Amazon Nova 2 customization, you can use the power of thinking models on your specific domains through techniques such as SFT and Reinforcement Fine-Tuning (RFT). For SFT, a key requirement to unlock these gains is the availability of golden CoT traces in the SFT dataset, that is, thinking traces from a strong teacher model that have been further validated and cleaned.

In this post, we explore an idea for generating thinking tokens for datasets that lack reasoning traces in SFT customization. We first examine the reasoning suppression problem, then introduce Self-Distilled Reasoning (SDR), validate it across three benchmarks, and provide practical recommendations. SDR re-uses the chain of thought from the base Amazon Nova 2 Lite model as a stand-in for non-reasoning datasets. Through our experiments we make a few observations on the impact of introducing SDR, from improving target performance to mitigating catastrophic forgetting. This is in line with a growing body of work that is discovering the importance of distilling information from a model into itself during training, otherwise known as self-distillation.