Warm start
Beginning training from an existing model’s weights instead of from scratch.
Think of it like
Like starting a novel from a detailed outline rather than a blank page.
Example
Rather than train a new model from zero, a team warm-starts from an open-weights checkpoint and fine-tunes on their data.
How it actually works
A warm start initializes from pretrained weights so the model already “knows” language and only needs to adapt, saving enormous compute and data versus a cold start. It underpins transfer learning and fine-tuning — but the starting model’s biases and limitations carry over, for better and worse.
For product teams
Why almost nobody trains from scratch anymore — you stand on an existing model’s shoulders.
For engineers
Initializing parameters from a pretrained checkpoint rather than random init to accelerate convergence.
Related
- Checkpoint — The snapshot you start from.
- Pretraining — The pretraining that made it.
- Base model — Reusing knowledge across tasks.
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