diff --git a/src/forge/actors/trainer.py b/src/forge/actors/trainer.py
index 0368fac4d..b148a7f94 100644
--- a/src/forge/actors/trainer.py
+++ b/src/forge/actors/trainer.py
@@ -6,6 +6,7 @@
 
 import math
 import os
+import re
 import time
 from collections.abc import Mapping
 from dataclasses import dataclass, field, fields
@@ -215,7 +216,7 @@ async def push_weights(self, policy_version: int) -> None:
             )
         hf_state_dict = self.engine.checkpointer.sd_adapter.to_hf(flattened_state_dict)
         # TODO: Figure out how to gracefully handle which model to-vLLM conversion is needed
-        vllm_ready_hf_sd = _qwen3_hf_to_vllm(sd=hf_state_dict, num_layers=28)
+        vllm_ready_hf_sd = _qwen3_hf_to_vllm(sd=hf_state_dict)
         key = f"{self.state_dict_key}{DELIM}{policy_version}"
         start_time = time.time()
         await ts.put_state_dict(state_dict=vllm_ready_hf_sd, key=key)
@@ -230,7 +231,7 @@ async def cleanup(self) -> None:
             self.engine.checkpointer.close()
 
 
-def _qwen3_hf_to_vllm(sd: dict[str, Tensor], num_layers: int) -> dict[str, Tensor]:
+def _qwen3_hf_to_vllm(sd: dict[str, Tensor]) -> dict[str, Tensor]:
     """Convert transformers state dict to vLLM format. Specifically, this fuses
     QKV projection and MLP gate_up_proj layers.
 
@@ -243,6 +244,17 @@ def _qwen3_hf_to_vllm(sd: dict[str, Tensor], num_layers: int) -> dict[str, Tenso
     """
     load_sd = {}
 
+    # Infer num_layers from the state dict by finding the highest layer index
+    layer_indices = []
+    pattern = re.compile(r"model\.layers\.(\d+)\.")
+    for k in sd.keys():
+        m = pattern.search(k)
+        if m:
+            layer_indices.append(int(m.group(1)))
+    if not layer_indices:
+        raise ValueError("Could not infer num_layers from state dict keys.")
+    num_layers = max(layer_indices) + 1
+
     # Copy over directly mapped keys
     for k in sd:
         if any(