diff --git a/python/freetoken/models/glm4_moe/moe.py b/python/freetoken/models/glm4_moe/moe.py
index 8e595ee7c7..648eb9c32a 100644
--- a/python/freetoken/models/glm4_moe/moe.py
+++ b/python/freetoken/models/glm4_moe/moe.py
@@ -32,10 +32,8 @@ def __init__(self, config: ModelConfig, layer_id: int, *, prefix: str = ""):
         self.topk_group = config.topk_group
 
         self.gate = LinearReplicated(config.hidden_size, config.num_experts, has_bias=False)
-        # DeepSeek-style selection bias; a registered buffer in HF (kept fp32 there). We
-        # store it in the model's bf16 dtype and upcast at use, which is exact enough for
-        # the argmax-style top-k selection.
-        self.e_score_correction_bias = torch.empty(config.num_experts)
+        # Keep selection bias in fp32: rounding can change the selected experts.
+        self.e_score_correction_bias = torch.empty(config.num_experts, dtype=torch.float32)
 
         # The offload cache indexes experts by *MoE* layer (global layer minus
         # first_k_dense_replace), matching how the loader packs the expert banks. The
diff --git a/python/freetoken/models/glm4_moe/weight.py b/python/freetoken/models/glm4_moe/weight.py
index 38b17a0536..aa6538dcf9 100644
--- a/python/freetoken/models/glm4_moe/weight.py
+++ b/python/freetoken/models/glm4_moe/weight.py
@@ -168,11 +168,11 @@ def _iter_resident_weights(reader, config, primary) -> Iterator[tuple[str, torch
             for proj in ("gate_proj", "up_proj", "down_proj"):
                 yield from _iter_nvfp4_resident(reader, f"{m}.{proj}", f"{m}.{proj}")
         else:
-            # router (bf16 gate + fp32 selection bias -> bf16) and shared expert.
+            # router (bf16 gate + fp32 selection bias) and shared expert.
             yield f"{m}.gate.weight", reader.get(f"{m}.gate.weight")
             yield (
                 f"{m}.e_score_correction_bias",
-                reader.get(f"{m}.gate.e_score_correction_bias").to(torch.bfloat16),
+                reader.get(f"{m}.gate.e_score_correction_bias").to(torch.float32),
             )
             s = f"{m}.shared_experts"
             for proj in ("gate_proj", "up_proj", "down_proj"):