$$
\nonumber
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$$
Self-Play Fine-Tuning
#
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
https://arxiv.org/abs/2401.01335
Suppose $f(x, y)$ is some score model. Let $Y \sim \pi _ \theta(Y | X)$.
$$
\L = -E\s{f(X, Y)} + \gamma \d{\pi _ \theta(y | X)}{\pi _ {\phi}(y | X)}
$$
Now transform the loss function:
$$
\begin{aligned}
-\L & = E \s{f(X, Y) - \gamma \log \frac{\pi _ \theta(Y|X)}{\pi _ \phi(Y | X)}}\\
& = -\gamma E \s{\frac{1}{\gamma}f(X, Y) + \log \pi _ \theta(Y | X) - \log \pi _ \phi(Y | X)}\\
& = - \gamma E \s{\log \pi _ \theta (Y | X) - \p{\log \pi _ \phi(Y | X) - \gamma^{-1}f(X, Y) }}
\end{aligned}
$$
Again, there is a one-to-one relationship between optimal policy $\pi _ * (y | x)$ and optimal score $f(x, y)$.
$$
\pi _ * (y | x) = \pi _ \phi(y | x) \exp\p{\gamma^{-1} f(x, y)} / Z(x) \implies f(x, y) = \gamma \log\frac{\pi _ * (y | x)}{\pi _ \phi(y | x)} + \gamma \log Z(x)
$$
We can learn the optimal score by minimizing:
$$
E\s{\ell \p{f(X, Y _ w) - f(X, Y)}} = E \s{\ell \c{\gamma \log \frac{\pi _ * (Y _ w | X)}{\pi _ \phi(Y _ w|X)} - \gamma \log \frac{\pi _ * (Y | X)}{\pi _ \phi(Y | X)}}}
$$
This is analogous to DPO, but starting from optimizing an integral probability metric (IPM).