diff --git a/scripts/run-qwen3.5-35B-A3B-sft.sh b/scripts/run-qwen3.5-35B-A3B-sft.sh
index 6893133924..b5910ae5b8 100644
--- a/scripts/run-qwen3.5-35B-A3B-sft.sh
+++ b/scripts/run-qwen3.5-35B-A3B-sft.sh
@@ -46,6 +46,18 @@ CKPT_ARGS=(
    --save-interval 20
 )
 
+SFT_MAX_PROMPT_TOKENS=${SFT_MAX_PROMPT_TOKENS:-8192}
+MAX_TOKENS_PER_GPU=${MAX_TOKENS_PER_GPU:-8192}
+CP_SIZE=${CP_SIZE:-1}
+echo "SFT_MAX_PROMPT_TOKENS: ${SFT_MAX_PROMPT_TOKENS}"
+echo "MAX_TOKENS_PER_GPU: ${MAX_TOKENS_PER_GPU}"
+echo "CP_SIZE: ${CP_SIZE}"
+
+if (( SFT_MAX_PROMPT_TOKENS > MAX_TOKENS_PER_GPU * CP_SIZE )); then
+  echo "Invalid config: SFT_MAX_PROMPT_TOKENS (${SFT_MAX_PROMPT_TOKENS}) must be <= MAX_TOKENS_PER_GPU * CP_SIZE (${MAX_TOKENS_PER_GPU} * ${CP_SIZE} = $((MAX_TOKENS_PER_GPU * CP_SIZE)))."
+  exit 1
+fi
+
 SFT_ARGS=(
    --rollout-function-path slime.rollout.sft_rollout.generate_rollout
    --prompt-data ${BASE_FOLDER}/openhermes2_5.parquet
@@ -54,6 +66,7 @@ SFT_ARGS=(
    --num-epoch 3
    --rollout-batch-size 128
    --global-batch-size 128
+   --rollout-max-prompt-len ${SFT_MAX_PROMPT_TOKENS}
 
    --loss-type sft_loss
    --loss-mask-type qwen3_5
@@ -66,7 +79,7 @@ PERF_ARGS=(
    --tensor-model-parallel-size 2
    --sequence-parallel
    --pipeline-model-parallel-size 1
-   --context-parallel-size 1
+   --context-parallel-size ${CP_SIZE}
    --expert-model-parallel-size 8
    --expert-tensor-parallel-size 1
 
@@ -76,7 +89,7 @@ PERF_ARGS=(
 
    # --micro-batch-size 1
    --use-dynamic-batch-size
-   --max-tokens-per-gpu 8192
+   --max-tokens-per-gpu ${MAX_TOKENS_PER_GPU}
 )
 
 OPTIMIZER_ARGS=(
@@ -88,7 +101,7 @@ OPTIMIZER_ARGS=(
    --weight-decay 0.1
    --adam-beta1 0.9
    --adam-beta2 0.98
-   
+
    --use-distributed-optimizer
    --optimizer-cpu-offload
    --overlap-cpu-optimizer-d2h-h2d
diff --git a/slime/utils/data.py b/slime/utils/data.py
index 4bb81e5677..74cdce0301 100644
--- a/slime/utils/data.py
+++ b/slime/utils/data.py
@@ -82,13 +82,19 @@ def filter_long_prompt(origin_samples: list[Sample], tokenizer, processor, max_l
     if max_length is None:
         return origin_samples
 
-    if not isinstance(origin_samples[0].prompt, str):
+    # Keep filtering compatible with both text datasets and chat-message datasets used by SFT.
+    first_prompt = origin_samples[0].prompt
+    supports_text_prompt = isinstance(first_prompt, str)
+    supports_message_prompt = isinstance(first_prompt, list)
+    if not (supports_text_prompt or supports_message_prompt):
+        # Unknown prompt shapes are skipped to avoid breaking custom data pipelines.
         logger.warning(
-            "Skipping max_length check for list prompt. Set apply_chat_template=True to enable length filtering."
+            "Skipping max_length check for unsupported prompt type %s. Set apply_chat_template=True to enable length filtering.",
+            type(first_prompt),
         )
         return origin_samples
 
-    if processor:
+    if processor and supports_text_prompt:
         # Use processor only for samples with actual multimodal content; use batched tokenizer for text-only.
         text_only = []
         multimodal = []
@@ -113,7 +119,8 @@ def filter_long_prompt(origin_samples: list[Sample], tokenizer, processor, max_l
                 input_ids = processor_output["input_ids"][0]
                 if len(input_ids) <= max_length:
                     filtered_samples.append(sample)
-    else:
+    elif supports_text_prompt:
+        # Fast path for text-only prompts: tokenize in batch for throughput.
         prompts = [sample.prompt for sample in origin_samples]
         input_ids_list = tokenizer(prompts, add_special_tokens=False)["input_ids"]
         filtered_samples = [
@@ -121,6 +128,21 @@ def filter_long_prompt(origin_samples: list[Sample], tokenizer, processor, max_l
             for sample, input_ids in zip(origin_samples, input_ids_list, strict=True)
             if len(input_ids) <= max_length
         ]
+    else:
+        # `messages` datasets used by SFT rollout pass a list[dict] conversation as prompt.
+        # Use chat template tokenization to keep filtering behavior aligned with training tokenization.
+        filtered_samples = []
+        for sample in origin_samples:
+            tools = sample.metadata.get("tools") if isinstance(sample.metadata, dict) else None
+            input_ids = tokenizer.apply_chat_template(
+                sample.prompt,
+                tokenize=True,
+                add_generation_prompt=False,
+                tools=tools,
+                return_dict=False,
+            )
+            if len(input_ids) <= max_length:
+                filtered_samples.append(sample)
 
     logger.info(f"Filtered {len(origin_samples) - len(filtered_samples)} samples longer than max_length={max_length}.")