Bottom line: Successful domain specialization of LLMs requires careful tuning of learning rate, data-mixing ratios, and checkpoint selection to avoid catastrophic forgetting.
Amazon Nova Forge enables engineers to train specialized Large Language Models based on Amazon Nova. The greatest challenge lies in building domain expertise without destroying the model’s general capabilities.
Amazon Nova Forge enables training of frontier models from early model checkpoints, combines proprietary data with Amazon Nova-curated training data, and enables secure deployment on AWS. A core feature is Data Mixing – blending proprietary training data with curated datasets during training – to impart domain knowledge to the model while preserving broad reasoning, instruction-following, and language capabilities.
The greatest challenge is catastrophic forgetting: when training on domain-specific data, a model can overwrite its general capabilities from pre-training. A customer service model fine-tuned on support tickets may thereby lose the ability for multi-turn conversations or reasoning about ambiguous requests. This creates a trade-off between flexibility (domain learning) and stability (preservation of general capabilities).
Learning rate is the most critical hyperparameter for all customization techniques. A rate that is too high leads to overshoot and rapid forgetting of fundamental capabilities, while a rate that is too low results in inefficient training. The optimal value depends on data distribution, mixing ratio, and training technique. With Data Mixing, sensitivity increases additionally. Amazon Nova Forge provides calibrated service defaults for each training technique that account for these interactions.
Beyond learning rate, batch size, checkpoint selection, and the strategy for avoiding catastrophic forgetting also influence success. Common mistakes – wrong learning rates, poor checkpoint selection, or insufficient data mixing – lead to wasted compute. Early identification of these issues prevents costly failed training runs.
Source: aws.amazon.com · Published June 2, 2026
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