Train Dpo

train-dpo — preference optimization

Template: scripts/train_dpo.py. Required dataset columns: prompt, chosen, rejected.

Sequence

1. Audit dataset — python scripts/inspect_dataset.py <dataset>. Verify the three required columns exist and aren't empty / duplicated.
2. Pick the SFT base — DPO requires a model that has already been instruct- tuned. If the user gives a base model that hasn't been SFT'd, surface this and propose either: (a) SFT first on a small prompt-completion dataset, or (b) start from a published instruct checkpoint (e.g. SmolLM2-360M-Instruct).
3. Config — start from configs/sft_default.yaml; for DPO add/override:

   train:
     beta: 0.1                  # KL coefficient — typical range 0.05 – 0.5
     max_length: 2048
     max_prompt_length: 1024
     learning_rate: 5.0e-7      # DPO is much more sensitive than SFT
     num_train_epochs: 1
   

4. Smoke test — same --max-steps 20 --max-samples 256 discipline.
5. Watch reward margin and KL in Trackio. If rewards/margin stays at 0 the dataset is uninformative. If KL blows up, lower the learning rate.
6. Scale locally with accelerate or via scripts/launch_hf_job.py.

DPO doubles GPU memory (policy + reference model). Size up one tier from what you'd pick for SFT of the same model.