According to DeepLearning.AI, researchers have demonstrated that large language models (LLMs) can be trained using 4-bit FP4 precision for matrix multiplications, which account for 95% of training computation, without any loss of accuracy compared to the standard BF16 format. This breakthrough dramatically reduces computational requirements and hardware costs, potentially accelerating AI-powered blockchain and cryptocurrency analytics platforms by lowering entry barriers for decentralized AI projects (Source: DeepLearning.AI, May 31, 2025).

According to Andrej Karpathy on Twitter, a significant new paradigm—system prompt learning—is emerging in large language model (LLM) training, distinct from pretraining and fine-tuning methods (source: @karpathy, May 11, 2025). While pretraining builds foundational knowledge and fine-tuning shapes habitual behavior by altering model parameters, system prompt learning enables dynamic behavioral adaptation without changing parameters. For crypto traders, this development could accelerate AI-driven trading bots' adaptability to new market conditions, enhancing execution strategies and potentially impacting short-term volatility as AI trading tools become more responsive (source: @karpathy, May 11, 2025).