Exploring self-distilled reasoning for supervised fine-tuning with Amazon Nova

2026-07-21 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, extended

Summary

Self-Distilled Reasoning (SDR) is introduced as a method to enhance Supervised Fine-Tuning (SFT) for Amazon Nova 2 models, particularly when training datasets lack high-quality chain-of-thought (CoT) reasoning traces. Traditional SFT without CoT can lead to reasoning suppression and catastrophic forgetting, causing general math performance to drop from 70 percent to 0 percent. SDR mitigates this by generating and prepending CoT traces from the base Amazon Nova 2 Lite model to the SFT dataset. Experiments on MedMCQA, CoCoHD, and Invoice-OCR benchmarks demonstrate SDR's effectiveness, improving target performance by over 6.5 percent on average and recovering general math capabilities to approximately 68 percent. This zero-cost, annotation-free approach outperforms model merging in preserving both target and general performance, even with partial or missing reasoning data, maintaining math control accuracy at 65–72 percent across various data compositions.

Key takeaway

For Machine Learning Engineers fine-tuning Amazon Nova 2 models, if your SFT datasets lack reasoning traces or cause general performance regression, you should implement Self-Distilled Reasoning (SDR). This approach, using Nova 2 Lite-generated traces, prevents catastrophic forgetting and improves target performance by over 6.5 percent without annotation costs. Apply SDR as a default SFT recipe, especially when reasoning coverage is below 75 percent, to maintain general capabilities and avoid complex model merging trade-offs.

Key insights

Self-Distilled Reasoning (SDR) prevents catastrophic forgetting and boosts SFT performance by augmenting datasets with self-generated reasoning traces.

Principles

• Reasoning traces provide implicit policy regularization.
• Process supervision enhances model generalization.
• Self-distillation preserves base model capabilities.

Method

SDR generates reasoning traces from a base model, prepends them to SFT dataset outputs, then fine-tunes the model with reasoning mode active, supervising both reasoning and output tokens.

In practice

• Generate traces using the base Amazon Nova 2 Lite model.
• Apply SDR when reasoning coverage is below 75 percent.
• Combine SDR with data mixing for multi-skill preservation.

Topics

• Self-Distilled Reasoning
• Supervised Fine-Tuning
• Amazon Nova 2
• Chain-of-Thought
• Catastrophic Forgetting
• Model Distillation

Code references

• gtfintechlab/CoCoHD

Best for: Machine Learning Engineer, AI Scientist, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.