Fine-Tuning Warm-Up Stage
(Redirected from Gradual Fine-Tuning Phase)
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A Fine-Tuning Warm-Up Stage is a preparatory machine learning training stage that gradually introduces task-specific learning before full fine-tuning processes.
- AKA: Gradual Fine-Tuning Phase, Adaptive Learning Ramp-Up.
- Context:
- It can typically prevent Catastrophic Forgetting through gradual adaptation mechanisms.
- It can typically stabilize Learning Dynamics using progressive difficulty increases.
- It can typically improve Final Model Performance via controlled parameter adjustments.
- It can typically reduce Training Instability through incremental learning rates.
- It can typically facilitate Knowledge Transfer from pre-trained representations.
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- It can often incorporate Curriculum Learning Strategies for sample ordering tasks.
- It can often balance Generic Knowledge Retention with task-specific adaptations.
- It can often optimize Computational Resource Usage through efficient schedulings.
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- It can range from being a Brief Fine-Tuning Warm-Up Stage to being an Extended Fine-Tuning Warm-Up Stage, depending on its temporal duration length.
- It can range from being a Linear Fine-Tuning Warm-Up Stage to being a Non-Linear Fine-Tuning Warm-Up Stage, depending on its progression curve shape.
- It can range from being a Single-Task Fine-Tuning Warm-Up Stage to being a Multi-Task Fine-Tuning Warm-Up Stage, depending on its task diversity scope.
- It can range from being a Parameter-Frozen Fine-Tuning Warm-Up Stage to being a Full-Parameter Fine-Tuning Warm-Up Stage, depending on its trainable parameter proportion.
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- It can integrate with Learning Rate Schedulers for optimization control tasks.
- It can connect to Model Checkpoint Systems for progress monitoring purposes.
- It can interface with Hyperparameter Optimization Frameworks for configuration tuning.
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- Example(s):
- Language Model Warm-Up Stages, such as:
- Vision Model Warm-Up Stages, such as:
- Multimodal Warm-Up Stages, such as:
- ...
- Counter-Example(s):
- Cold-Start Training Stages, which begin at full learning rates.
- Pre-Training Stages, which train from random initializations.
- Inference Stages, which involve no parameter updates.
- See: Fine-Tuning, Transfer Learning, Curriculum Learning, Learning Rate Schedule, Catastrophic Forgetting, Model Adaptation, Training Strategy, Machine Learning Pipeline.