diff --git a/examples/sft/gsm8k/run_qwen3_5_35b_a3b_megatron.sh b/examples/sft/gsm8k/run_qwen3_5_35b_a3b_megatron.sh
new file mode 100755
index 00000000000..91e865f30dd
--- /dev/null
+++ b/examples/sft/gsm8k/run_qwen3_5_35b_a3b_megatron.sh
@@ -0,0 +1,154 @@
+#!/usr/bin/env bash
+# Qwen3.5-35B-A3B SFT with Megatron backend, packed sequences (THD format)
+#
+# This is the counterpart to run_qwen3_5_397b_a17b_megatron.sh, which runs the same
+# architecture family in bshd (padded) format. Qwen3.5 uses Gated Delta Net (GDN) linear
+# attention, and GDN did not support packed sequences until
+# https://github.com/NVIDIA/Megatron-LM/pull/2644 -- merged 2026-04-07 and first shipped
+# in a megatron-core release in 0.18.0. On 0.18.0+ THD works, so the padding no longer has
+# to be computed.
+#
+# Requirements:
+#   - 16+ GPUs (80GB each, e.g. 2x8 H100/H200/H20) for the parallelism below
+#   - megatron-core >= 0.18.0    <-- GDN packed-sequence support; earlier releases
+#                                    (0.16.x, 0.17.x) silently lack the THD branch in
+#                                    megatron/core/ssm/gated_delta_net.py
+#   - Additional packages on top of the base image:
+#       pip install --upgrade transformers
+#       pip install flash-linear-attention
+#
+# Data:
+#   python3 examples/data_preprocess/gsm8k_multiturn_sft.py --local_save_dir ~/dataset
+#
+# THD (packed sequence) settings -- all three are needed:
+#   model.use_remove_padding=True    unpad on the model side
+#   engine.use_remove_padding=True   thd compute format in the Megatron engine
+#   data.use_dynamic_bsz=True        batch by token budget instead of sample count;
+#                                    this is what turns unpadding into a throughput win
+#   data.max_token_len_per_gpu       per-GPU token budget. The effective micro-batch
+#                                    budget is max_token_len_per_gpu * CP_SIZE, so it must
+#                                    be >= data.max_length / CP_SIZE for a longest-sample
+#                                    micro-batch to fit.
+#
+# attention_backend is deliberately not set: the default (Megatron-Bridge) path already
+# pins AttnBackend.flash. Do not switch this example to the deprecated
+# engine.vanilla_mbridge=True path without also setting
+#   +engine.override_transformer_config.attention_backend=flash
+# There, attention_backend falls back to megatron's `auto`, which prefers cuDNN fused
+# attention on Hopper+; cuDNN's SDPA backward has an uninitialized-workspace defect on
+# Hopper (fixed in cuDNN 9.18) that makes THD backward produce huge dK/dV and then NaN
+# gradients. See https://github.com/NVIDIA/TransformerEngine/issues/2186.
+#
+# Tested parallelism config (16 GPUs / 2 nodes):
+#   TP=2 PP=2 CP=2 EP=8
+
+set -xeuo pipefail
+
+# ============================================================
+# Distributed
+# ============================================================
+NUM_GPUS=${NUM_GPUS:-8}
+MASTER_ADDR=${MASTER_ADDR:-localhost}
+MASTER_PORT=${MASTER_PORT:-29500}
+NNODES=${NNODES:-2}
+NODE_RANK=${NODE_RANK:-0}
+
+# ============================================================
+# Data
+# ============================================================
+DATASET_DIR=${DATASET_DIR:-~/dataset}
+TRAIN_FILES=${TRAIN_FILES:-${DATASET_DIR}/train.parquet}
+
+# ============================================================
+# Model
+# ============================================================
+MODEL_PATH=${MODEL_PATH:-Qwen/Qwen3.5-35B-A3B}
+
+# ============================================================
+# Parallelism
+# ============================================================
+TP_SIZE=${TP_SIZE:-2}
+PP_SIZE=${PP_SIZE:-2}
+VPP_SIZE=${VPP_SIZE:-null}
+CP_SIZE=${CP_SIZE:-2}
+EP_SIZE=${EP_SIZE:-8}
+ETP_SIZE=${ETP_SIZE:-1}
+
+# ============================================================
+# Training
+# ============================================================
+TRAIN_BATCH_SIZE=${TRAIN_BATCH_SIZE:-64}
+MAX_LENGTH=${MAX_LENGTH:-2048}
+# Token budget per GPU per micro-batch. Effective budget is this * CP_SIZE.
+MAX_TOKEN_LEN_PER_GPU=${MAX_TOKEN_LEN_PER_GPU:-2048}
+LR=${LR:-2e-5}
+MIN_LR=${MIN_LR:-2e-6}
+DTYPE=${DTYPE:-bfloat16}
+
+BACKEND=megatron
+RESUME_MODE=${RESUME_MODE:-disable}
+
+project_name=verl_sft_qwen3_5
+exp_name=qwen3_5-35b-a3b-${BACKEND}-thd-tp${TP_SIZE}-pp${PP_SIZE}-cp${CP_SIZE}-ep${EP_SIZE}
+ckpts_home=${ckpts_home:-~/verl/checkpoints/${project_name}/${exp_name}}
+mkdir -p "${ckpts_home}"
+
+# ============================================================
+# Engine config
+# ============================================================
+# Key Qwen3.5 THD settings:
+#   engine.use_remove_padding=True   - thd compute format (needs megatron-core >= 0.18.0)
+#   attention_backend unset          - default bridge path already pins flash (see header)
+ENGINE_CONFIG="\
+    engine=${BACKEND} \
+    optim=${BACKEND} \
+    optim.lr=${LR} \
+    optim.min_lr=${MIN_LR} \
+    optim.lr_warmup_steps=10 \
+    optim.weight_decay=0.1 \
+    optim.betas='[0.9,0.95]' \
+    optim.clip_grad=1.0 \
+    optim.lr_warmup_init=0 \
+    optim.lr_decay_style=cosine \
+    engine.tensor_model_parallel_size=${TP_SIZE} \
+    engine.pipeline_model_parallel_size=${PP_SIZE} \
+    engine.virtual_pipeline_model_parallel_size=${VPP_SIZE} \
+    engine.context_parallel_size=${CP_SIZE} \
+    engine.expert_model_parallel_size=${EP_SIZE} \
+    engine.expert_tensor_parallel_size=${ETP_SIZE} \
+    engine.dtype=${DTYPE} \
+    engine.use_remove_padding=True \
+    +engine.override_transformer_config.recompute_method=uniform \
+    +engine.override_transformer_config.recompute_granularity=full \
+    +engine.override_transformer_config.recompute_num_layers=1"
+
+# ============================================================
+# Launch
+# ============================================================
+torchrun \
+    --nproc_per_node=${NUM_GPUS} \
+    --nnodes=${NNODES} \
+    --node_rank=${NODE_RANK} \
+    --master_addr=${MASTER_ADDR} \
+    --master_port=${MASTER_PORT} \
+    -m verl.trainer.sft_trainer \
+    data.train_files="${TRAIN_FILES}" \
+    data.train_batch_size=${TRAIN_BATCH_SIZE} \
+    data.max_length=${MAX_LENGTH} \
+    data.pad_mode=no_padding \
+    data.truncation=error \
+    data.use_dynamic_bsz=True \
+    data.max_token_len_per_gpu=${MAX_TOKEN_LEN_PER_GPU} \
+    data.messages_key=messages \
+    model.path=${MODEL_PATH} \
+    model.use_remove_padding=True \
+    model.trust_remote_code=True \
+    ${ENGINE_CONFIG} \
+    trainer.test_freq=-1 \
+    trainer.save_freq=500 \
+    trainer.logger='["console"]' \
+    trainer.project_name="${project_name}" \
+    trainer.experiment_name="${exp_name}" \
+    trainer.total_epochs=1 \
+    trainer.default_local_dir="${ckpts_home}" \
+    trainer.resume_mode=${RESUME_MODE}
diff --git a/examples/sft/gsm8k/run_qwen3_5_397b_a17b_megatron.sh b/examples/sft/gsm8k/run_qwen3_5_397b_a17b_megatron.sh
index c6d9bfedd8b..a304e0788ae 100644
--- a/examples/sft/gsm8k/run_qwen3_5_397b_a17b_megatron.sh
+++ b/examples/sft/gsm8k/run_qwen3_5_397b_a17b_megatron.sh
@@ -11,14 +11,17 @@
 #   - Megatron-LM==0.16.0
 #
 # Qwen3.5 architecture notes:
-#   Qwen3.5 uses Gated Delta Net (GDN) linear attention which currently does
-#   NOT support packed sequences (THD format) in Megatron-LM. Therefore:
+#   Qwen3.5 uses Gated Delta Net (GDN) linear attention. GDN did not support packed
+#   sequences (THD format) on the Megatron-LM 0.16.0 pinned above, so this script runs
+#   in bshd (padded) format:
 #     - engine.use_remove_padding=False  (forces bshd compute format)
 #     - data.use_dynamic_bsz=False       (required for bshd mode)
 #
-#   Once https://github.com/NVIDIA/Megatron-LM/pull/2644 is merged, THD
-#   format will be supported and engine.use_remove_padding can be set to True
-#   for better performance.
+#   https://github.com/NVIDIA/Megatron-LM/pull/2644 (the PR this note used to wait on)
+#   merged 2026-04-07 and first shipped in megatron-core 0.18.0, so THD is available
+#   there. This script is left on bshd because it is pinned to Megatron-LM 0.16.0 and its
+#   parallelism config was validated that way; see
+#   run_qwen3_5_35b_a3b_megatron.sh for a THD configuration on megatron-core >= 0.18.0.
 #
 # MTP (Multi-Token Prediction) notes:
 #   - model.mtp.enable=True               enables MTP module
@@ -91,7 +94,7 @@ MTP_LOSS_SCALING_FACTOR=${MTP_LOSS_SCALING_FACTOR:-0.1}
 # Engine config
 # ============================================================
 # Key Qwen3.5 settings:
-#   engine.use_remove_padding=False   - GDN requires bshd format (no THD)
+#   engine.use_remove_padding=False   - bshd format (THD needs megatron-core >= 0.18.0)
 #   engine.vanilla_mbridge=True       - use mbridge (not megatron-bridge)
 ENGINE_CONFIG="\
     engine=${BACKEND} \