diff --git a/torchrl/objectives/llm/grpo.py b/torchrl/objectives/llm/grpo.py
index 2cdab05be9f..51158a419a0 100644
--- a/torchrl/objectives/llm/grpo.py
+++ b/torchrl/objectives/llm/grpo.py
@@ -348,16 +348,10 @@ def forward(self, tensordict: TensorDictBase) -> GRPOLossOutput:
             raise ValueError(
                 f"advantage and log_weight must have the same number of dimensions, got {advantage.ndim=} and {log_weight.ndim=}"
             )
-        gain1 = log_weight.exp() * advantage
-
-        log_weight_clip = log_weight.clamp(*self._clip_bounds)
-        clip_fraction = (log_weight_clip != log_weight).to(log_weight.dtype).mean()
-        ratio = log_weight_clip.exp()
-        gain2 = ratio * advantage
-
-        # Token-level objective: compute min over clipped/unclipped at the token level
-        gain = torch.stack([gain1, gain2], -1).min(dim=-1).values
-        td_out = TensorDict({"loss_objective": -gain})
+        loss_objective, clip_fraction = self._compute_policy_objective(
+            log_weight, advantage
+        )
+        td_out = TensorDict({"loss_objective": loss_objective})
         td_out.set("clip_fraction", clip_fraction)
         td_out.set("kl_approx", kl_approx.detach().mean())  # for logging
 
@@ -406,6 +400,21 @@ def forward(self, tensordict: TensorDictBase) -> GRPOLossOutput:
         del tensordict["_cur_log_prob"]
         return GRPOLossOutput.from_tensordict(td_out)
 
+    def _compute_policy_objective(
+        self, log_weight: torch.Tensor, advantage: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Default GRPO objective: PPO-style min between unclipped and clipped ratios.
+
+        Returns (loss_objective, clip_fraction).
+        """
+        gain1 = log_weight.exp() * advantage
+        log_weight_clip = log_weight.clamp(*self._clip_bounds)
+        clip_fraction = (log_weight_clip != log_weight).to(log_weight.dtype).mean()
+        ratio = log_weight_clip.exp()
+        gain2 = ratio * advantage
+        gain = torch.stack([gain1, gain2], -1).min(dim=-1).values
+        return -gain, clip_fraction
+
     def _get_entropy(
         self, dist: d.Distribution, adv_shape: torch.Size
     ) -> torch.Tensor | TensorDict:
@@ -594,6 +603,27 @@ def __init__(
         return coeff * kl_penalty, kl_penalty
 
 
+class CISPO(GRPOLoss):
+    """CISPO (Clipped Importance Sampling Policy Optimization).
+
+    Inherits the GRPO pipeline (masking, ESS, entropy, optional KL penalties) but
+    replaces the PPO-style min with a clipped-importance objective:
+        loss = - clip(weight, [1 - eps_low, 1 + eps_high]) * advantage
+
+    See MiniMax-M1 (CISPO) [arXiv](https://arxiv.org/html/2506.13585).
+    """
+
+    def _compute_policy_objective(
+        self, log_weight: torch.Tensor, advantage: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # CISPO: use clipped importance weights directly
+        log_weight_clip = log_weight.clamp(*self._clip_bounds)
+        clip_fraction = (log_weight_clip != log_weight).to(log_weight.dtype).mean()
+        ratio = log_weight_clip.exp()
+        gain = ratio * advantage
+        return -gain, clip_fraction
+
+
 class MCAdvantage(Transform):
     """Monte-Carlo advantage computation engine.