diff --git a/applications/ColossalChat/coati/models/loss.py b/applications/ColossalChat/coati/models/loss.py
index 927dfd5a89b6..04cb6ef24f95 100755
--- a/applications/ColossalChat/coati/models/loss.py
+++ b/applications/ColossalChat/coati/models/loss.py
@@ -207,9 +207,14 @@ def forward(
     ) -> torch.Tensor:
         chosen_logp = chosen_logp.to(dtype=torch.float32)
         reject_logp = reject_logp.to(dtype=torch.float32)
-        chosen_odds = chosen_logp - torch.log(-torch.exp(chosen_logp) + 1.0001)
+        # Clamp log-probabilities to avoid numerical instability in log-odds computation.
+        # log_odds = log(p) - log(1 - p) = logp - log1p(-exp(logp))
+        eps = 1e-6
+        chosen_logp = torch.clamp(chosen_logp, max=-eps)
+        reject_logp = torch.clamp(reject_logp, max=-eps)
+        chosen_odds = chosen_logp - torch.log1p(-torch.exp(chosen_logp))
         chosen_odds_masked = torch.sum(chosen_odds * chosen_loss_mask.float()) / torch.sum(chosen_loss_mask)
-        reject_odds = reject_logp - torch.log(-torch.exp(reject_logp) + 1.0001)
+        reject_odds = reject_logp - torch.log1p(-torch.exp(reject_logp))
         reject_odds_masked = torch.sum(reject_odds * reject_loss_mask.float()) / torch.sum(reject_loss_mask)
         log_odds_ratio = chosen_odds_masked - reject_odds_masked
         ratio = torch.log(torch.nn.functional.sigmoid(log_odds_ratio))