According to @DeepLearningAI, researchers found a method to estimate how many bits a model memorizes from its training data. Source: DeepLearning.AI on Twitter, Aug 28, 2025. In tests on hundreds of GPT-2-style models trained on synthetic data and FineWeb subsets, the approach compares the negative log likelihood of a trained model to a stronger model. Source: DeepLearning.AI on Twitter, Aug 28, 2025. The post did not provide performance numbers, release details, or market implications, so no direct crypto trading signal is indicated. Source: DeepLearning.AI on Twitter, Aug 28, 2025.

According to Stanford AI Lab (@StanfordAILab), their recent research demonstrates that large language models (LLMs) can memorize long sequences of text verbatim, and this capability is closely linked to the model’s overall performance and generalization abilities (source: ai.stanford.edu/blog/verbatim-). For crypto trading algorithms utilizing LLMs, this finding suggests that models may retain and recall specific market data patterns or trading strategies from training data, potentially influencing prediction accuracy and risk of data leakage. Traders deploying AI-driven strategies should account for LLMs’ memorization characteristics to optimize signal reliability and minimize exposure to overfitting (source: Stanford AI Lab, April 30, 2025).