Add GRPO and Support RLVR for PPO (#6186)

* add grpo, support rlvr

* add grpo, support rlvr

* tested deepseek r1 pipeline

* add ci

* verify grpo r1

* verify grpo r1

* update readme, remove unused code

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* remove path

* clean code

* fix circular import

* fix ci OOM

* fix ci OOM

* skip kto tp, fix qwen generation

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

applications/ColossalChat/coati/models/rlvr_reward_model.py

@@ -0,0 +1,50 @@
+"""
+reward model
+"""
+
+from typing import Callable, List, Optional
+
+import torch
+
+
+class RLVRRewardModel:
+    """
+    RLVRReward model class. Support varifiable reward.
+
+    Args:
+        reward_fn_list List: list of reward functions
+        **kwargs: all other kwargs as in reward functions
+    """
+
+    def __init__(self, reward_fn_list: List[Callable], **kwargs) -> None:
+        self.reward_fn_list = reward_fn_list
+        self.kwargs = kwargs
+
+    def __call__(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        response_start: List = None,
+        response_end: List = None,
+        gt_answer: List = None,
+    ) -> torch.Tensor:
+        # apply varifiable reward
+        bs = input_ids.size(0)
+        rewards = torch.zeros(bs, device=input_ids.device)
+        for i in range(bs):
+            for reward_fn in self.reward_fn_list:
+                rewards[i] += reward_fn(
+                    input_ids[i],
+                    attention_mask[i],
+                    response_start=response_start[i],
+                    response_end=response_end[i],
+                    gt_answer=gt_answer[i],
+                    **self.kwargs,
+                )
+        return rewards
+
+    def to(self, device):
+        return self
+
+    def eval(self):
+        return self