diff --git a/docker/validator/Dockerfile b/docker/validator/Dockerfile
index 13454dd..795212c 100644
--- a/docker/validator/Dockerfile
+++ b/docker/validator/Dockerfile
@@ -19,11 +19,9 @@ WORKDIR /app
 COPY docker/validator/pyproject.toml docker/validator/uv.lock /app/
 RUN uv sync --frozen --no-dev --no-install-project
 
-# Pre-cache SentenceTransformer models (avoids runtime download). The
-# canonical Qwen3 model is always loaded; the bge-small bundle is for the
-# optional ORO-1474 shadow scorer enabled via SHADOW_SCORE_MODEL at runtime.
+# Pre-cache the canonical sentence model (avoids runtime download).
 ARG HF_TOKEN=""
-RUN HF_HUB_ENABLE_HF_TRANSFER=0 HF_TOKEN=${HF_TOKEN} .venv/bin/python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('Qwen/Qwen3-Embedding-0.6B'); SentenceTransformer('BAAI/bge-small-en-v1.5')"
+RUN HF_HUB_ENABLE_HF_TRANSFER=0 HF_TOKEN=${HF_TOKEN} .venv/bin/python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('BAAI/bge-small-en-v1.5')"
 
 # Copy validator code
 COPY subnet/ /app/subnet/
diff --git a/src/agent/rewards/orm.py b/src/agent/rewards/orm.py
index f67c10f..6845cf1 100644
--- a/src/agent/rewards/orm.py
+++ b/src/agent/rewards/orm.py
@@ -3,10 +3,10 @@
 import os
 from collections import Counter
 
-SENTENCE_MODEL_NAME = "Qwen/Qwen3-Embedding-0.6B"
-TITLE_SIM_THRESHOLD = 0.7
+SENTENCE_MODEL_NAME = "BAAI/bge-small-en-v1.5"
+TITLE_SIM_THRESHOLD = 0.72
 
-# ORO-1474 shadow scoring: when SHADOW_SCORE_MODEL is set, encode the same
+# Shadow scoring: when SHADOW_SCORE_MODEL is set, encode the same
 # (product_title, gt_title) pair with the candidate model and log the
 # canonical + shadow cosine similarity so we can grep CW Logs Insights and
 # replay any threshold offline before promoting a model swap. Threshold is
@@ -33,10 +33,7 @@ def _get_sentence_model():
         try:
             from sentence_transformers import SentenceTransformer
 
-            _sentence_model = SentenceTransformer(
-                SENTENCE_MODEL_NAME,
-                model_kwargs={"torch_dtype": "bfloat16"},
-            )
+            _sentence_model = SentenceTransformer(SENTENCE_MODEL_NAME)
         except ImportError:
             _sentence_model_unavailable = True
             return None
diff --git a/tests/test_scoring_perf.py b/tests/test_scoring_perf.py
index a134060..faaf83b 100644
--- a/tests/test_scoring_perf.py
+++ b/tests/test_scoring_perf.py
@@ -52,7 +52,7 @@ def test_non_gt_calls_encode(mock_get):
     )
 
     assert m.encode.call_count >= 1
-    assert hits["title"] == 1   # similarity 0.95 >= TITLE_SIM_THRESHOLD (0.7)
+    assert hits["title"] == 1   # similarity 0.95 >= TITLE_SIM_THRESHOLD (0.72)
     assert hits["price"] == 1   # 15 <= 20
     assert hits["service"] == 1
     assert score == 1.0         # 3/3