You can fine-tune on multiple tasks simultaneously. Mix different instruction types in your training data.
Benefits:
- One model handles many tasks
- Tasks can transfer and help each other
Risks:
- Task interference (one task hurts another)
- Need careful balancing of task proportions
Start with single-task fine-tuning to establish baselines, then experiment with multi-task.