diff --git a/torchrl/objectives/llm/grpo.py b/torchrl/objectives/llm/grpo.py
index 51158a419a0..add8e7b14d8 100644
--- a/torchrl/objectives/llm/grpo.py
+++ b/torchrl/objectives/llm/grpo.py
@@ -82,6 +82,10 @@ class GRPOLoss(LossModule):
             - float x: symmetric clipping [1 - x, 1 + x] (default: 0.2)
             - tuple (eps_low, eps_high): asymmetric clipping [1 - eps_low, 1 + eps_high] as in DAPO Clip-Higher
               recommended defaults from DAPO: (0.20, 0.28); see Eq. (10) in the paper.
+        kl_mask_threshold (float | None, optional): enable token-wise trust-region filtering (KL-Mask).
+            When set, tokens with 0.5 * (log(pi_theta/pi_ref))^2 > kl_mask_threshold are masked out from the loss.
+            This stabilizes updates by skipping tokens that drifted too far from the reference distribution
+            (see table and description; enables per-token trust region).
         entropy_bonus (bool, optional): if ``True``, an entropy bonus will be added to the
             loss to favour exploratory policies.
         samples_mc_entropy (int, optional): if the distribution retrieved from the policy
@@ -142,6 +146,7 @@ def __init__(
         actor_network: LLMWrapperBase | None = None,
         *,
         clip_epsilon: float | tuple[float, float] = 0.2,
+        kl_mask_threshold: float | None = None,
         entropy_bonus: bool = True,
         samples_mc_entropy: int = 1,
         entropy_coeff: float = 0.01,
@@ -161,6 +166,7 @@ def __init__(
         self.samples_mc_entropy = samples_mc_entropy
         self.entropy_coeff = entropy_coeff
         self.reduction = reduction if reduction is not None else "mean"
+        self.kl_mask_threshold = kl_mask_threshold
 
         # Determine device and register clip epsilon as buffer
         if device is None:
@@ -335,6 +341,32 @@ def forward(self, tensordict: TensorDictBase) -> GRPOLossOutput:
             tensordict, adv_shape=advantage.shape[:-1]
         )
         mask = dist.mask
+
+        # Optional per-token trust-region filtering (KL-Mask) vs reference policy
+        if self.kl_mask_threshold is not None and self.kl_mask_threshold > 0:
+            try:
+                ref_log_prob = tensordict.get(
+                    self.tensor_keys.ref_log_probs,
+                    as_padded_tensor=True,
+                    padding_side="left",
+                    padding_value=0.0,
+                )
+            except KeyError:
+                ref_log_prob = None
+            cur_log_prob = tensordict.get("_cur_log_prob", None)
+            if (ref_log_prob is not None) and (cur_log_prob is not None):
+                # Align to valid tokens only (safety)
+                cur_log_prob_masked = torch.where(
+                    expand_as_right(mask, cur_log_prob), cur_log_prob, 0.0
+                )
+                ref_log_prob_masked = torch.where(
+                    expand_as_right(mask, ref_log_prob), ref_log_prob, 0.0
+                )
+                log_is_ref = cur_log_prob_masked - ref_log_prob_masked
+                kl_token = 0.5 * (log_is_ref**2)
+                tr_mask = kl_token <= self.kl_mask_threshold
+                # Combine with attention mask
+                mask = mask & tr_mask
         # ESS for logging
         with torch.no_grad():
             # In theory, ESS should be computed on particles sampled from the same source. Here we sample according