Update README.md

README.md

@@ -25,7 +25,7 @@ The proposed methodology involves the following steps:
 7. Optimize the policy \\(\pi\\) to minimize the necessary correction = \\(min_{\pi} ( log(\pi(y/x) - log(\pi(y’/x) )\\)
 
 ## Domain specific custom objective
-This framework allows for the selection of \\(P\\), offering the flexibility to choose. If additional prior assumptions are available, they can be integrated. For instance, a prior concerning the distribution of response lengths could be included, limiting the model to produce responses of a certain length. If \\(P(y)\\) = \\(pi(y)\\) * \\(l(x)\\), where \\(l(x)\\) is a prior specific to a target domain, the optimization function becomes \\(min_{\pi} ( log(\pi(y/x)) - log(\pi(y’/x) ) + log(l(y)) - log(l(y’)) \\). This indicates the aim to minimize the extra loss specific to the target domain.
+This framework allows for the selection of \\(P\\), offering the flexibility to choose. If additional prior assumptions are available, they can be integrated. For instance, a prior concerning the distribution of response lengths could be included, limiting the model to produce responses of a certain length. If \\(P(y)\\) = \\(\pi(y)\\) * \\(l(y)\\), where \\(l(y)\\) is a prior specific to a target domain, the optimization function becomes \\(min_{\pi} ( log(\pi(y/x)) - log(\pi(y’/x) ) + log(l(y)) - log(l(y’)) \\). This indicates the aim to minimize the extra loss specific to the target domain.
    
 ## Connection with Direct Preference Optimization (DPO) and Contrastive Preference Learning (CPL)
 The proposed approach has a direct connection to the [DPO](https://arxiv.org/pdf/2305.18290) and [CPL](https://arxiv.org/pdf/2310.13639) frameworks.