[espadrine]

DPO is as close to RL as RLHF. The latter also uses the LLM as a reward model.

I'm not a fan of the RL/SL dichotomy, because the line gets so foggy. If you squint, every loss is a negative reward, and every policy improvement a supervised target.

Still, what the code does isn't what is described in the paper that the page links to.

[nextaccountic]

> I'm not a fan of the RL/SL dichotomy, because the line gets so foggy. If you squint, every loss is a negative reward, and every policy improvement a supervised target.

Isn't this just because reinforcement learning and supervised learning are both optimization problems?

[espadrine]

In part, yes! But also because what used to define it was the human-curated datasets: SL contained input/output pairs, while RL contained episodes with sporadic rewards.

Nowadays, many datasets have different forms or are synthetic. DPO uses datasets with both positive and negative examples (instead of just a target output as with traditional SL); RLHF uses synthetic rewards.