I have trained a Pi0.5 LoRA model on 200 episodes for 40,000 steps, and it works well in most cases. However, during inference, there are still a few error cases. I have collected new correction data for these specific failure cases.
Can I perform incremental LoRA fine-tuning based on my already trained 200-episode model?
If yes, could you please give me some advice on the number of training steps for the new small dataset, relative to my original 40,000 steps?
If I re-train the model from scratch on the full dataset every time I collect new data, it will take an extremely long time. So I really want to use incremental learning efficiently.
Thank you so much for your help!
I have trained a Pi0.5 LoRA model on 200 episodes for 40,000 steps, and it works well in most cases. However, during inference, there are still a few error cases. I have collected new correction data for these specific failure cases.
Can I perform incremental LoRA fine-tuning based on my already trained 200-episode model?
If yes, could you please give me some advice on the number of training steps for the new small dataset, relative to my original 40,000 steps?
If I re-train the model from scratch on the full dataset every time I collect new data, it will take an extremely long time. So I really want to use incremental learning efficiently.
Thank you so much for your help!