diff --git a/README.md b/README.md
index ddb2bac7..ef90b729 100644
--- a/README.md
+++ b/README.md
@@ -312,7 +312,7 @@ Atropos handles tokenization in two places:
    - Recommendation: set `--openai.tokenizer_name` explicitly to match the student serving model.
 
 2. **Teacher top-k parsing path**
-   - Teacher responses are parsed into token ids/logprobs in `BaseEnv.get_teacher_logprobs`.
+   - Teacher responses are fetched/parsed in `TeacherClient.get_teacher_logprobs` (called by `BaseEnv`).
    - The parser maps teacher token strings into ids using the environment tokenizer (`self.tokenizer`) and then aligns to student sequence length.
 
 Because distillation is token-position based, keeping tokenizer families compatible is strongly recommended, especially for cross-model distillation.
diff --git a/atroposlib/envs/base.py b/atroposlib/envs/base.py
index 74383075..b816ce66 100644
--- a/atroposlib/envs/base.py
+++ b/atroposlib/envs/base.py
@@ -990,7 +990,12 @@ class BaseEnv(ABC):
 
             if self.config.include_messages and group.get("messages") is None:
                 group["messages"] = [
-                    self.tokenizer.decode(group["tokens"][i])
+                    [
+                        {
+                            "role": "user",
+                            "content": self.tokenizer.decode(group["tokens"][i]),
+                        }
+                    ]
                     for i in range(len(group["tokens"]))
                 ]
 
diff --git a/atroposlib/envs/server_handling/teacher_client.py b/atroposlib/envs/server_handling/teacher_client.py
index a929d7dc..af5897b4 100644
--- a/atroposlib/envs/server_handling/teacher_client.py
+++ b/atroposlib/envs/server_handling/teacher_client.py
@@ -1,5 +1,5 @@
 import os
-from typing import Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import aiohttp
 
@@ -15,6 +15,28 @@ class TeacherClient:
         self.config = config
         self.tokenizer = tokenizer
         self.logger = logger
+        if self.config.distillation_enabled:
+            self._validate_distillation_config()
+
+    def _validate_distillation_config(self) -> None:
+        if not self.config.teacher_base_url:
+            raise ValueError("Distillation requires `teacher_base_url` to be set.")
+        if self.config.teacher_top_k <= 0:
+            raise ValueError(
+                f"Distillation requires `teacher_top_k > 0`, got {self.config.teacher_top_k}."
+            )
+        student_model_name = getattr(self.config, "model_name", None)
+        if (
+            self.config.teacher_model_name
+            and student_model_name
+            and self.config.teacher_model_name != student_model_name
+        ):
+            self.logger.warning(
+                "Cross-model distillation configured (teacher=%s, student=%s). "
+                "Token-level alignment quality depends on tokenizer compatibility.",
+                self.config.teacher_model_name,
+                student_model_name,
+            )
 
     async def get_teacher_logprobs(
         self,
@@ -109,7 +131,7 @@ class TeacherClient:
                         pass
 
                     if messages_list and i < len(messages_list):
-                        messages = list(messages_list[i])
+                        messages = self._normalize_messages(messages_list[i], full_text)
                         if self.config.teacher_system_prompt:
                             messages = [
                                 {
@@ -176,6 +198,36 @@ class TeacherClient:
             self.logger.error("Error fetching teacher logprobs: %s", e)
             return [], []
 
+    def _normalize_messages(
+        self, raw_messages: Any, fallback_text: str
+    ) -> List[Dict[str, str]]:
+        """
+        Normalize environment message payloads for chat/completions teacher fallback.
+
+        Accepts already-structured message lists, plain strings, or unknown structures.
+        """
+        if isinstance(raw_messages, str):
+            return [{"role": "user", "content": raw_messages}]
+
+        if isinstance(raw_messages, list):
+            normalized: List[Dict[str, str]] = []
+            for msg in raw_messages:
+                if (
+                    isinstance(msg, dict)
+                    and "role" in msg
+                    and "content" in msg
+                    and isinstance(msg["content"], str)
+                ):
+                    normalized.append(
+                        {"role": str(msg["role"]), "content": msg["content"]}
+                    )
+                elif isinstance(msg, str):
+                    normalized.append({"role": "user", "content": msg})
+            if normalized:
+                return normalized
+
+        return [{"role": "user", "content": fallback_text}]
+
     def _align_teacher_topk_to_tokens(
         self,
         seq_token_ids: List[List[int]],