Gradient Descent

Gradient descent is the iterative optimization algorithm that updates a neural network's weights by stepping in the direction opposite to the gradient of the loss function, gradually moving toward lower loss. Variants include batch gradient descent (uses the full dataset per step), stochastic gradient descent or SGD (uses one example), and mini-batch gradient descent (uses a small batch — the standard choice). Modern LLM training uses mini-batch sizes from 1M tokens (Llama 3) to 4M tokens or more, distributed across hundreds or thousands of GPUs. The gradient is computed via backpropagation, then scaled by a learning rate before being applied. Pure gradient descent has been largely supplanted by adaptive optimizers like Adam and AdamW that adjust per-parameter learning rates based on gradient history, dramatically improving convergence speed and stability on deep models. AI governance teams document the optimizer choice and hyperparameters as part of their training audit trail because optimizer behavior affects training stability, convergence, and the final model's properties.

Gradient-trained models governed by Centralpoint: Centralpoint operates above whatever optimization recipe produced your models, with consistent metering and audit logging across the LLM stack. The model-agnostic platform routes to OpenAI, Claude, Gemini, LLAMA, embedded models, keeps prompts local, and deploys chatbots through one line of JavaScript.


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