[worker] fix: Fix missing rollout_log_probs argument in policy loss functions (#3274)

### What does this PR do?

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In the recent PR:

- #2953,

the file `workers/actor/dp_actor.py` was updated so that
`rollout_log_probs` is passed to `policy_loss_fn`:


https://github.com/volcengine/verl/blob/38d23914ee512a125e00763fe3ddcc8df4319346/verl/workers/actor/dp_actor.py#L448-L456

In that PR, the "vanilla" policy loss function was modified to accept
`rollout_log_probs` as an argument. However, other policy loss functions
(e.g., "gspo") were not updated accordingly, which leads to an error
such as:

```
TypeError: compute_policy_loss_gspo() got an unexpected keyword argument 'rollout_log_probs'
```

when setting `config.policy_loss.loss_mode` to one of these
alternatives.

Therefore, in this PR, `rollout_log_probs` is also added as an argument
to the other policy loss functions.



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Author: kAIto47802

verl/trainer/ppo/core_algos.py

@@ -41,6 +41,7 @@
         torch.Tensor,  # response_mask
         str,  # loss_agg_mode
         Optional[DictConfig | AlgoConfig],  # config
+        torch.Tensor | None,  # rollout_log_probs
     ],
     tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor],
 ]
@@ -820,7 +821,7 @@ def compute_policy_loss_vanilla(
     response_mask: torch.Tensor,
     loss_agg_mode: str = "token-mean",
     config: Optional[DictConfig | AlgoConfig] = None,
-    rollout_log_probs=None,
+    rollout_log_probs: torch.Tensor | None = None,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
     """
     Compute the clipped policy objective and related metrics for PPO.
@@ -909,6 +910,7 @@ def compute_policy_loss_gspo(
     response_mask: torch.Tensor,
     loss_agg_mode: str = "seq-mean-token-mean",
     config: Optional[DictConfig | ActorConfig] = None,
+    rollout_log_probs: torch.Tensor | None = None,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
     """
     Compute the clipped policy objective and related metrics for GSPO.
@@ -967,7 +969,15 @@ def compute_policy_loss_gspo(
 
 
 @register_policy_loss("gpg")
-def compute_policy_loss_gpg(old_log_prob, log_prob, advantages, response_mask, loss_agg_mode="token-mean", config=None):
+def compute_policy_loss_gpg(
+    old_log_prob: torch.Tensor,
+    log_prob: torch.Tensor,
+    advantages: torch.Tensor,
+    response_mask: torch.Tensor,
+    loss_agg_mode: str = "token-mean",
+    config: Optional[DictConfig | AlgoConfig] = None,
+    rollout_log_probs: torch.Tensor | None = None,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
     """Adapted from
     https://github.com/AMAP-ML/GPG/blob/main/VisualThinker-R1-Zero/src/open-r1-multimodal/src/open_r1/trainer/grpo_trainer.py#L495
     Args:
@@ -995,6 +1005,7 @@ def compute_policy_loss_clip_cov(
     response_mask: torch.Tensor,
     loss_agg_mode: str = "token-mean",
     config: Optional[DictConfig | AlgoConfig] = None,
+    rollout_log_probs: torch.Tensor | None = None,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
     """
     Compute the clipped policy objective and related metrics for Clip-Cov.
@@ -1089,6 +1100,7 @@ def compute_policy_loss_kl_cov(
     response_mask: torch.Tensor,
     loss_agg_mode: str = "token-mean",
     config: Optional[DictConfig | AlgoConfig] = None,
+    rollout_log_probs: torch.Tensor | None = None,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
     """
     Compute the clipped policy objective and related metrics for Clip-Cov.
@@ -1160,6 +1172,7 @@ def compute_policy_loss_geo_mean(
     response_mask: torch.Tensor,
     loss_agg_mode: str = "token-mean",
     config: Optional[DictConfig | AlgoConfig] = None,
+    rollout_log_probs: torch.Tensor | None = None,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
     """
     Compute the clipped policy objective and related metrics for GMPO.