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The training loop is the control flow that drives iterative updates of model parameters, typically consisting of a loop over steps: forward propagation to compute the loss, backward propagation to compute gradients, and the optimizer updating parameters.
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10/27/2026
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VerifiedFine-tuning continues to update parameters on top of pre-trained weights through the backpropagation algorithm, adapting the model to a specific domain or task style.75% similarUnverifiedIn the reinforcement learning pipeline, the model continuously generates candidate outputs (rollouts), which are evaluated and scored before being fed back into the training process to update the policy.74% similarUnverifiedThe training loop paradigm in machine learning includes defining a task, executing, evaluating results, adjusting based on feedback, and executing again.74% similarUnverifiedScheduled Sampling is a compromise approach to mitigating exposure bias, gradually introducing the model's own predictions as input during the later stages of training.73% similarUnverifiedThe trend of integrating inference and training refers to training and inference sharing model definitions, parallel strategies and scheduling infrastructure to reduce duplicated construction of two systems; capabilities accumulated by vLLM on the inference side, such as PagedAttention and continuous batching, are gradually extending into the post-training stage.73% similar
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