diff --git a/tinker_cookbook/renderers.py b/tinker_cookbook/renderers.py
index 349c17c..3a11029 100644
--- a/tinker_cookbook/renderers.py
+++ b/tinker_cookbook/renderers.py
@@ -34,6 +34,7 @@ class Message(TypedDict):
     content: str
     tool_calls: NotRequired[list[ToolCall]]
     thinking: NotRequired[str]
+    trainable: NotRequired[bool]
 
 
 class TrainOnWhat(StrEnum):
@@ -42,6 +43,7 @@ class TrainOnWhat(StrEnum):
     ALL_MESSAGES = "all_messages"
     ALL_TOKENS = "all_tokens"
     ALL_USER_AND_SYSTEM_MESSAGES = "all_user_and_system_messages"
+    CUSTOMIZED = "customized"
 
 
 class Renderer:
@@ -101,6 +103,10 @@ def build_supervised_example(
         train_on_what: an enum that controls how the weights are assigned to the tokens.
             - TrainOnWhat.LAST_ASSISTANT_MESSAGE: only the last assistant message is used for training
             - TrainOnWhat.ALL_ASSISTANT_MESSAGES: all assistant messages are used for training
+            - TrainOnWhat.ALL_MESSAGES: all messages are used for training
+            - TrainOnWhat.ALL_TOKENS: all tokens are used for training
+            - TrainOnWhat.ALL_USER_AND_SYSTEM_MESSAGES: all user and system messages are used for training
+            - TrainOnWhat.CUSTOMIZED: each message has a trainable field, and the weights are assigned based on the trainable field
         messages: a list of messages to render.
 
     Returns:
@@ -110,29 +116,48 @@ def build_supervised_example(
     """
     tokens_weights = [(token, 0) for token in start_tokens]
     for idx, message in enumerate(messages[:-1]):
-        ob_part, action_part, action_tail = render_message(idx, message)
-        if train_on_what == TrainOnWhat.LAST_ASSISTANT_MESSAGE:
-            tokens_weights.extend([(token, 0) for token in ob_part + action_part])
-        elif train_on_what == TrainOnWhat.ALL_ASSISTANT_MESSAGES:
-            tokens_weights += [(token, 0) for token in ob_part]
-            # TODO: look at the previous action tail and its overlap with the current action part
-            # and put weight of 1 on those tokens too.
-            is_assistant = message["role"] == "assistant"
-            tokens_weights += [(token, int(is_assistant)) for token in action_part]
-        elif train_on_what == TrainOnWhat.ALL_MESSAGES:
-            tokens_weights += [(token, 0) for token in ob_part]
-            tokens_weights += [(token, 1) for token in action_part]
-        elif train_on_what == TrainOnWhat.ALL_TOKENS:
-            tokens_weights += [(token, 1) for token in ob_part + action_part]
-        elif train_on_what == TrainOnWhat.ALL_USER_AND_SYSTEM_MESSAGES:
-            tokens_weights += [(token, 0) for token in ob_part]
-            is_user_or_system = message["role"] in ["user", "system"]
-            tokens_weights += [(token, int(is_user_or_system)) for token in action_part]
+        if train_on_what == TrainOnWhat.CUSTOMIZED:
+            assert "trainable" in message, (
+                "When using CUSTOMIZED train_on_what, each message must have a trainable field: True if loss is applied on this message, False otherwise"
+            )
         else:
-            raise ValueError(f"Unknown train_on_what: {train_on_what}")
-    ob_part, action_part, action_tail = render_message(len(messages) - 1, messages[-1])
-    tokens_weights.extend([(token, 0) for token in ob_part])
-    tokens_weights.extend([(token, 1) for token in action_part + action_tail])
+            assert "trainable" not in message, (
+                "When using non-CUSTOMIZED train_on_what, each message must not have a trainable field. Either change train_on_what to CUSTOMIZED or remove the trainable field from the message"
+            )
+
+        is_last_message = idx == len(messages) - 1
+        is_assistant = message["role"] == "assistant"
+        is_user_or_system = message["role"] in ["user", "system"]
+
+        # only apply weight to observation part if train_on_what is ALL_TOKENS
+        ob_part, action_part, action_tail = render_message(idx, message)
+        ob_weight = int(train_on_what == TrainOnWhat.ALL_TOKENS)
+        tokens_weights += [(token, ob_weight) for token in ob_part]
+
+        action_tokens = action_part
+        # action tail is effectively the stop_token and the start token for the next turn
+        # e.g. \n\nUser:
+        if is_last_message:
+            action_tokens += action_tail
+
+        match train_on_what:
+            case TrainOnWhat.LAST_ASSISTANT_MESSAGE:
+                action_has_weight = is_last_message and is_assistant
+            case TrainOnWhat.ALL_ASSISTANT_MESSAGES:
+                action_has_weight = is_assistant
+            case TrainOnWhat.ALL_MESSAGES:
+                action_has_weight = True
+            case TrainOnWhat.ALL_TOKENS:
+                action_has_weight = True
+            case TrainOnWhat.ALL_USER_AND_SYSTEM_MESSAGES:
+                action_has_weight = is_user_or_system
+            case TrainOnWhat.CUSTOMIZED:
+                action_has_weight = message.get("trainable", False)
+            case _:
+                raise ValueError(f"Unknown train_on_what: {train_on_what}")
+
+        tokens_weights += [(token, int(action_has_weight)) for token in action_tokens]
+
     tokens, weights = zip(*tokens_weights, strict=True)
     return torch.tensor(tokens), torch.tensor(weights)