diff --git a/tests/buffer/reward_shaping_mapper_test.py b/tests/buffer/reward_shaping_mapper_test.py
new file mode 100644
index 0000000000..d44122f649
--- /dev/null
+++ b/tests/buffer/reward_shaping_mapper_test.py
@@ -0,0 +1,74 @@
+import unittest
+from copy import deepcopy
+from typing import List
+
+import torch
+
+from trinity.buffer.pipelines.experience_pipeline import ExperienceOperator
+from trinity.common.config import OperatorConfig
+from trinity.common.experience import EID, Experience
+
+
+def get_experiences(task_num: int, repeat_times: int = 1, step_num: int = 1) -> List[Experience]:
+    """Generate a list of experiences for testing."""
+    return [
+        Experience(
+            eid=EID(task=i, run=j, step=k),
+            tokens=torch.zeros((5,)),
+            prompt_length=4,
+            reward=j,
+            logprobs=torch.tensor([0.1]),
+            info={
+                "llm_quality_score": i,
+                "llm_difficulty_score": k,
+            },
+        )
+        for i in range(task_num)
+        for j in range(repeat_times)
+        for k in range(step_num)
+    ]
+
+
+class TestRewardShapingMapper(unittest.TestCase):
+    def test_basic_usage(self):
+        # test input cache
+        op_configs = [
+            OperatorConfig(
+                name="reward_shaping_mapper",
+                args={
+                    "reward_shaping_configs": [
+                        {
+                            "stats_key": "llm_quality_score",
+                            "op_type": "ADD",
+                            "weight": 1.0,
+                        },
+                        {
+                            "stats_key": "llm_difficulty_score",
+                            "op_type": "MUL",
+                            "weight": 0.5,
+                        },
+                    ]
+                },
+            )
+        ]
+        ops = ExperienceOperator.create_operators(op_configs)
+        self.assertEqual(len(ops), 1)
+
+        op = ops[0]
+        task_num = 8
+        repeat_times = 4
+        step_num = 2
+        experiences = get_experiences(
+            task_num=task_num, repeat_times=repeat_times, step_num=step_num
+        )
+        res_exps, metrics = op.process(deepcopy(experiences))
+        self.assertEqual(len(res_exps), task_num * repeat_times * step_num)
+        self.assertEqual(len(metrics), 0)
+
+        for prev_exp, res_exp in zip(experiences, res_exps):
+            self.assertAlmostEqual(
+                (prev_exp.reward + prev_exp.info["llm_quality_score"])
+                * 0.5
+                * prev_exp.info["llm_difficulty_score"],
+                res_exp.reward,
+            )
diff --git a/trinity/buffer/operators/__init__.py b/trinity/buffer/operators/__init__.py
index b99f484f11..e7e7b0d73e 100644
--- a/trinity/buffer/operators/__init__.py
+++ b/trinity/buffer/operators/__init__.py
@@ -3,10 +3,12 @@
     ExperienceOperator,
 )
 from trinity.buffer.operators.filters.reward_filter import RewardFilter, RewardSTDFilter
+from trinity.buffer.operators.mappers.reward_shaping_mapper import RewardShapingMapper
 
 __all__ = [
     "ExperienceOperator",
     "EXPERIENCE_OPERATORS",
     "RewardFilter",
     "RewardSTDFilter",
+    "RewardShapingMapper",
 ]
diff --git a/trinity/buffer/operators/mappers/__init__.py b/trinity/buffer/operators/mappers/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/trinity/buffer/operators/mappers/reward_shaping_mapper.py b/trinity/buffer/operators/mappers/reward_shaping_mapper.py
new file mode 100644
index 0000000000..6e037d08c9
--- /dev/null
+++ b/trinity/buffer/operators/mappers/reward_shaping_mapper.py
@@ -0,0 +1,91 @@
+from typing import Dict, List, Optional, Tuple
+
+from trinity.buffer.operators import EXPERIENCE_OPERATORS, ExperienceOperator
+from trinity.common.constants import OpType
+from trinity.common.experience import Experience
+
+
+@EXPERIENCE_OPERATORS.register_module("reward_shaping_mapper")
+class RewardShapingMapper(ExperienceOperator):
+    """Re-shaping the existing rewards of experiences based on rules or other advanced methods.
+
+    Note:
+        This mapper assumes that the reward is already calculated and stored in the Experience object,
+        and the necessary stats are already calculated and stored in the Experience info field.
+    """
+
+    def __init__(self, reward_shaping_configs: Optional[List[Dict]] = None):
+        """Initializes the RewardShapingMapper.
+
+        Args:
+            reward_shaping_configs (list[dict], optional): A list of dictionaries containing reward shaping
+                configurations. Each dictionary should include the following keys:
+
+                - stats_key (str): The field key name of target stats used to shape the reward.
+                - op_type (str): The type of operator to apply between the reward and the target stats.
+                  Should be one of {"ADD", "SUB", "MUL", "DIV"}.
+                - weight (float): The weight for the target stats.
+
+                Example:
+                    [
+                        {
+                            "stats_key": "llm_quality_score",
+                            "op_type": "ADD",
+                            "weight": 1.0,
+                        }
+                    ]
+        """
+        if reward_shaping_configs is None:
+            reward_shaping_configs = []
+        self.reward_shaping_configs = reward_shaping_configs
+
+    def process(self, exps: List[Experience]) -> Tuple[List[Experience], Dict]:
+        res_exps = []
+        for exp in exps:
+            # skip experiences that don't have reward
+            if exp.reward is None:
+                continue
+            res_exp = exp
+            for reward_shaping_config in self.reward_shaping_configs:
+                res_exp = self._reward_shaping_single(res_exp, reward_shaping_config)
+            res_exps.append(res_exp)
+        return res_exps, {}
+
+    def _reward_shaping_single(self, exp: Experience, reward_shaping_config: Dict):
+        """Re-shapes the existing reward of one experience based on the given reward_shaping_config.
+
+        Args:
+            exp (Experience): The experience object whose reward is to be reshaped.
+            reward_shaping_config (dict): A dictionary containing the reward shaping configuration.
+                It should include the following keys:
+                - stats_key (str): The field key name of target stats used to shape the reward.
+                - op_type (str): The type of operator to apply between the reward and the target stats.
+                  Should be one of {"ADD", "SUB", "MUL", "DIV"}.
+                - weight (float): The weight for the target stats.
+
+        Returns:
+            Experience: The experience object with the reshaped reward.
+        """
+        tgt_stats = reward_shaping_config.get("stats_key", None)
+        op_type = OpType[reward_shaping_config.get("op_type", "ADD")]
+        weight = reward_shaping_config.get("weight", 1.0)
+        # if the target stats is not specified, skip the stats and return the original experience
+        if tgt_stats is None:
+            return exp
+        exp_info = exp.info
+        if exp_info is None or len(exp_info) == 0:
+            return exp
+        # if the target stats does not exist in the exp info, skip the stats and return the original experience
+        if tgt_stats not in exp_info:
+            return exp
+        if op_type == OpType.ADD:
+            exp.reward += weight * exp_info[tgt_stats]
+        elif op_type == OpType.MUL:
+            exp.reward *= weight * exp_info[tgt_stats]
+        elif op_type == OpType.SUB:
+            exp.reward -= weight * exp_info[tgt_stats]
+        elif op_type == OpType.DIV:
+            divisor = weight * exp_info[tgt_stats]
+            if divisor != 0:
+                exp.reward /= divisor
+        return exp