post-training
term_id: post_training
Category: training_techniques
Definition
Post-training is a critical stage in the machine learning lifecycle that occurs after the initial pre-training of a model on large-scale, general-purpose data. During this phase, the model undergoes further optimization, often involving fine-tuning, quantization, or alignment techniques like RLHF (Reinforcement Learning from Human Feedback). This process tailors the model’s capabilities to specific downstream applications, improves accuracy, reduces latency, or aligns outputs with human values, ensuring the model performs optimally in its intended deployment environment.
Summary
Post-training refers to the phase of refining a pre-trained model on specific datasets to adapt it to particular tasks or optimize performance.
Key Concepts
- Fine-tuning
- RLHF
- Quantization
- Adaptation
Use Cases
- Aligning LLMs with human preferences
- Optimizing model size for edge devices
- Specializing models for medical diagnosis