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The hybrid strategy involves first using public datasets to complete prototypes and baseline models, then targeted self-collection of edge-case data for incremental training, and finally continuously collecting hard samples in the real deployment environment for iterative improvement.
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10/6/2026
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UnverifiedA more mature approach is a hybrid strategy: using closed-source frontier models for long-tail complex tasks while using proprietary models to handle high-frequency, vertical, cost-sensitive core scenarios.80% similarVerifiedFine-tuning involves taking an existing large model and further training it with a specially prepared proprietary dataset, thereby changing the model's weight values75% similarUnverifiedGTF mixes true observations and the model's own predictions in proportion via a tunable mixing coefficient before feeding the result into the next step, biasing toward true data in the early stages of training and gradually increasing the proportion of the model's self-predictions as training progresses.74% similarUnverifiedDevin Fusion adopts an ensemble model scheduling mechanism that assigns different frontier models to handle the planning phase and the execution phase of a task.72% similarVerifiedTransfer learning refers to migrating model parameters pre-trained on large-scale datasets to a specific small-sample task for fine-tuning.71% similar
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