初始化项目，由ModelHub XC社区提供模型

Model: ayh015/myLightningOPD
Source: Original Platform

configs/lightning_opd/qwen3-30b-a3b-lightning-opd.py

@@ -0,0 +1,145 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+from pathlib import Path
+
+import slime.utils.external_utils.command_utils as U
+
+# Lightning OPD: response tokens + teacher logprobs are pre-computed in parquet.
+# No teacher server needed at training time -> all 8 GPUs go to the actor.
+#
+# Required env vars:
+#   SFT_CHECKPOINT      - path to the SFT checkpoint (HF format)
+#   LIGHTNING_OPD_DATA   - path to the precomputed parquet
+
+MODEL_NAME = "Qwen3-30B-A3B-Base-Open-Thoughts-Qwen3-30B-A3B-Thinking-2507-sft"
+MODEL_TYPE = "qwen3-30B-A3B"
+NUM_GPUS = 8
+SFT_CHECKPOINT = os.environ["SFT_CHECKPOINT"]
+
+
+def prepare():
+    U.convert_checkpoint(
+        model_name=MODEL_NAME,
+        megatron_model_type=MODEL_TYPE,
+        num_gpus_per_node=NUM_GPUS,
+        hf_checkpoint=SFT_CHECKPOINT,
+    )
+
+
+def execute(rerun=True):
+    load_save_path = f"/root/models/{MODEL_NAME}_ckpt__{Path(__file__).stem}/"
+
+    ckpt_args = (
+        f"--hf-checkpoint {SFT_CHECKPOINT} "
+        f"--ref-load /root/models/{MODEL_NAME}_torch_dist "
+        f"--load {load_save_path} "
+        f"--save {load_save_path} "
+        "--save-interval 10 "
+        "--save-retain-interval 100 "
+    )
+
+    rollout_args = (
+        f"--prompt-data {os.environ['LIGHTNING_OPD_DATA']} "
+        "--input-key prompt "
+        "--label-key label "
+        "--rollout-shuffle "
+        "--num-rollout 3000 "
+        "--rollout-batch-size 256 "
+        "--n-samples-per-prompt 1 "
+        "--rollout-max-response-len 4096 "
+        "--global-batch-size 256 "
+    )
+
+    rm_args = (
+        "--custom-rm-path slime.rollout.on_policy_distillation.reward_func "
+        "--custom-reward-post-process-path slime.rollout.on_policy_distillation.post_process_rewards "
+        "--include-verifiable-reward "
+    )
+
+    perf_args = (
+        "--tensor-model-parallel-size 4 "
+        "--sequence-parallel "
+        "--pipeline-model-parallel-size 1 "
+        "--context-parallel-size 1 "
+        "--expert-model-parallel-size 8 "
+        "--expert-tensor-parallel-size 1 "
+        "--recompute-granularity full "
+        "--recompute-method uniform "
+        "--recompute-num-layers 1 "
+        "--use-dynamic-batch-size "
+        "--max-tokens-per-gpu 12288 "
+    )
+
+    grpo_args = (
+        "--advantage-estimator on_policy_distillation "
+        "--use-kl-loss "
+        "--kl-loss-coef 0.00 "
+        "--kl-loss-type low_var_kl "
+        "--entropy-coef 0.00 "
+    )
+
+    optimizer_args = (
+        "--optimizer adam "
+        "--lr 2e-6 "
+        "--lr-decay-style constant "
+        "--weight-decay 0.1 "
+        "--adam-beta1 0.9 "
+        "--adam-beta2 0.98 "
+        "--optimizer-cpu-offload "
+        "--overlap-cpu-optimizer-d2h-h2d "
+        "--use-precision-aware-optimizer "
+    )
+
+    wandb_args = ""
+    if os.environ.get("WANDB_KEY"):
+        wandb_args = (
+            "--use-wandb "
+            "--wandb-project lightning-opd "
+            f"--wandb-group {Path(__file__).stem} "
+            f"--wandb-key {os.environ['WANDB_KEY']} "
+        )
+
+    sglang_args = (
+        "--rollout-num-gpus-per-engine 8 "
+        "--sglang-mem-fraction-static 0.8 "
+        "--sglang-cuda-graph-max-bs 32 "
+        "--sglang-max-running-requests 512 "
+        "--sglang-enable-metrics "
+    )
+
+    misc_args = (
+        "--attention-dropout 0.0 "
+        "--hidden-dropout 0.0 "
+        "--accumulate-allreduce-grads-in-fp32 "
+        "--attention-softmax-in-fp32 "
+        "--attention-backend flash "
+        "--actor-num-nodes 1 "
+        "--actor-num-gpus-per-node 8 "
+        "--rollout-num-gpus 0 "
+    )
+
+    train_args = (
+        f"{ckpt_args} "
+        f"{rollout_args} "
+        f"{rm_args} "
+        f"{grpo_args} "
+        f"{optimizer_args} "
+        f"{wandb_args} "
+        f"{perf_args} "
+        f"{sglang_args} "
+        f"{misc_args} "
+    )
+
+    U.execute_train(
+        rerun=rerun,
+        train_args=train_args,
+        num_gpus_per_node=NUM_GPUS,
+        megatron_model_type=MODEL_TYPE,
+    )
+
+
+if __name__ == "__main__":
+    prepare()
+    execute(rerun=False)

configs/lightning_opd/qwen3-4b-lightning-opd.py

@@ -0,0 +1,140 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+from pathlib import Path
+
+import slime.utils.external_utils.command_utils as U
+
+# Lightning OPD: response tokens + teacher logprobs are pre-computed in parquet.
+# No teacher server needed at training time -> all 8 GPUs go to the actor.
+#
+# Required env vars:
+#   SFT_CHECKPOINT      - path to the SFT checkpoint (HF format)
+#   LIGHTNING_OPD_DATA   - path to the precomputed parquet
+
+MODEL_NAME = "Qwen3-4B-Base-Open-Thoughts-Qwen3-8B-sft-3k"
+MODEL_TYPE = "qwen3-4B"
+NUM_GPUS = 4
+SFT_CHECKPOINT = os.environ["SFT_CHECKPOINT"]
+
+
+def prepare():
+    U.convert_checkpoint(
+        model_name=MODEL_NAME,
+        megatron_model_type=MODEL_TYPE,
+        num_gpus_per_node=NUM_GPUS,
+        hf_checkpoint=SFT_CHECKPOINT,
+    )
+
+
+def execute(rerun=True):
+    load_save_path = f"/root/models/{MODEL_NAME}_ckpt__{Path(__file__).stem}/"
+
+    ckpt_args = (
+        f"--hf-checkpoint {SFT_CHECKPOINT} "
+        f"--ref-load /root/models/{MODEL_NAME}_torch_dist "
+        f"--load {load_save_path} "
+        f"--save {load_save_path} "
+        "--save-interval 10 "
+        "--save-retain-interval 100 "
+    )
+
+    rollout_args = (
+        f"--prompt-data {os.environ['LIGHTNING_OPD_DATA']} "
+        "--input-key prompt "
+        "--label-key label "
+        "--rollout-shuffle "
+        "--num-rollout 150 "
+        "--rollout-batch-size 256 "
+        "--n-samples-per-prompt 1 "
+        "--rollout-max-response-len 4096 "
+        "--global-batch-size 256 "
+        "--rollout-temperature 0.8"
+    )
+
+    rm_args = (
+        "--custom-rm-path slime.rollout.on_policy_distillation.reward_func "
+        "--custom-reward-post-process-path slime.rollout.on_policy_distillation.post_process_rewards "
+        "--include-verifiable-reward "
+    )
+
+    perf_args = (
+        "--tensor-model-parallel-size 2 "
+        "--sequence-parallel "
+        "--pipeline-model-parallel-size 1 "
+        "--context-parallel-size 1 "
+        "--expert-model-parallel-size 1 "
+        "--expert-tensor-parallel-size 1 "
+        "--recompute-granularity full "
+        "--recompute-method uniform "
+        "--recompute-num-layers 1 "
+        "--use-dynamic-batch-size "
+        "--max-tokens-per-gpu 16384 "
+    )
+
+    grpo_args = (
+        "--advantage-estimator on_policy_distillation "
+        "--use-kl-loss "
+        "--kl-loss-coef 0.00 "
+        "--kl-loss-type low_var_kl "
+        "--entropy-coef 0.00 "
+    )
+
+    optimizer_args = (
+        "--optimizer adam "
+        "--lr 2e-6 "
+        "--lr-decay-style constant "
+        "--weight-decay 0.1 "
+        "--adam-beta1 0.9 "
+        "--adam-beta2 0.98 "
+    )
+
+    wandb_args = ""
+    if os.environ.get("WANDB_KEY"):
+        wandb_args = (
+            "--use-wandb "
+            "--wandb-project lightning-opd "
+            f"--wandb-group {Path(__file__).stem} "
+            f"--wandb-key {os.environ['WANDB_KEY']} "
+        )
+
+    sglang_args = (
+        "--rollout-num-gpus-per-engine 1 "
+        "--sglang-mem-fraction-static 0.4 "
+    )
+
+    misc_args = (
+        "--attention-dropout 0.0 "
+        "--hidden-dropout 0.0 "
+        "--accumulate-allreduce-grads-in-fp32 "
+        "--attention-softmax-in-fp32 "
+        "--attention-backend flash "
+        "--actor-num-nodes 1 "
+        "--actor-num-gpus-per-node 4 "
+        "--rollout-num-gpus 0 "
+    )
+
+    train_args = (
+        f"{ckpt_args} "
+        f"{rollout_args} "
+        f"{rm_args} "
+        f"{grpo_args} "
+        f"{optimizer_args} "
+        f"{wandb_args} "
+        f"{perf_args} "
+        f"{sglang_args} "
+        f"{misc_args} "
+    )
+
+    U.execute_train(
+        rerun=rerun,
+        train_args=train_args,
+        num_gpus_per_node=NUM_GPUS,
+        megatron_model_type=MODEL_TYPE,
+    )
+
+
+if __name__ == "__main__":
+    prepare()
+    execute(rerun=False)

configs/lightning_opd/qwen3-8b-lightning-opd.py

@@ -0,0 +1,139 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+from pathlib import Path
+
+import slime.utils.external_utils.command_utils as U
+
+# Lightning OPD: response tokens + teacher logprobs are pre-computed in parquet.
+# No teacher server needed at training time -> all 8 GPUs go to the actor.
+#
+# Required env vars:
+#   SFT_CHECKPOINT      - path to the SFT checkpoint (HF format)
+#   LIGHTNING_OPD_DATA   - path to the precomputed parquet
+
+MODEL_NAME = "Qwen3-8B-Base-Open-Thoughts-Qwen3-32B-sft-3k"
+MODEL_TYPE = "qwen3-8B"
+NUM_GPUS = 8
+SFT_CHECKPOINT = os.environ["SFT_CHECKPOINT"]
+
+
+def prepare():
+    U.convert_checkpoint(
+        model_name=MODEL_NAME,
+        megatron_model_type=MODEL_TYPE,
+        num_gpus_per_node=NUM_GPUS,
+        hf_checkpoint=SFT_CHECKPOINT,
+    )
+
+
+def execute(rerun=True):
+    load_save_path = f"/root/models/{MODEL_NAME}_ckpt__{Path(__file__).stem}/"
+
+    ckpt_args = (
+        f"--hf-checkpoint {SFT_CHECKPOINT} "
+        f"--ref-load /root/models/{MODEL_NAME}_torch_dist "
+        f"--load {load_save_path} "
+        f"--save {load_save_path} "
+        "--save-interval 10 "
+        "--save-retain-interval 100 "
+    )
+
+    rollout_args = (
+        f"--prompt-data {os.environ['LIGHTNING_OPD_DATA']} "
+        "--input-key prompt "
+        "--label-key label "
+        "--rollout-shuffle "
+        "--num-rollout 3000 "
+        "--rollout-batch-size 256 "
+        "--n-samples-per-prompt 1 "
+        "--rollout-max-response-len 4096 "
+        "--global-batch-size 256 "
+    )
+
+    rm_args = (
+        "--custom-rm-path slime.rollout.on_policy_distillation.reward_func "
+        "--custom-reward-post-process-path slime.rollout.on_policy_distillation.post_process_rewards "
+        "--include-verifiable-reward "
+    )
+
+    perf_args = (
+        "--tensor-model-parallel-size 4 "
+        "--sequence-parallel "
+        "--pipeline-model-parallel-size 1 "
+        "--context-parallel-size 1 "
+        "--expert-model-parallel-size 1 "
+        "--expert-tensor-parallel-size 1 "
+        "--recompute-granularity full "
+        "--recompute-method uniform "
+        "--recompute-num-layers 1 "
+        "--use-dynamic-batch-size "
+        "--max-tokens-per-gpu 16384 "
+    )
+
+    grpo_args = (
+        "--advantage-estimator on_policy_distillation "
+        "--use-kl-loss "
+        "--kl-loss-coef 0.00 "
+        "--kl-loss-type low_var_kl "
+        "--entropy-coef 0.00 "
+    )
+
+    optimizer_args = (
+        "--optimizer adam "
+        "--lr 2e-6 "
+        "--lr-decay-style constant "
+        "--weight-decay 0.1 "
+        "--adam-beta1 0.9 "
+        "--adam-beta2 0.98 "
+    )
+
+    wandb_args = ""
+    if os.environ.get("WANDB_KEY"):
+        wandb_args = (
+            "--use-wandb "
+            "--wandb-project lightning-opd "
+            f"--wandb-group {Path(__file__).stem} "
+            f"--wandb-key {os.environ['WANDB_KEY']} "
+        )
+
+    sglang_args = (
+        "--rollout-num-gpus-per-engine 1 "
+        "--sglang-mem-fraction-static 0.4 "
+    )
+
+    misc_args = (
+        "--attention-dropout 0.0 "
+        "--hidden-dropout 0.0 "
+        "--accumulate-allreduce-grads-in-fp32 "
+        "--attention-softmax-in-fp32 "
+        "--attention-backend flash "
+        "--actor-num-nodes 1 "
+        "--actor-num-gpus-per-node 8 "
+        "--rollout-num-gpus 0 "
+    )
+
+    train_args = (
+        f"{ckpt_args} "
+        f"{rollout_args} "
+        f"{rm_args} "
+        f"{grpo_args} "
+        f"{optimizer_args} "
+        f"{wandb_args} "
+        f"{perf_args} "
+        f"{sglang_args} "
+        f"{misc_args} "
+    )
+
+    U.execute_train(
+        rerun=rerun,
+        train_args=train_args,
+        num_gpus_per_node=NUM_GPUS,
+        megatron_model_type=MODEL_TYPE,
+    )
+
+
+if __name__ == "__main__":
+    prepare()
+    execute(rerun=False)

configs/models/qwen3-30B-A3B.sh

@@ -0,0 +1,49 @@
+NLAYERS=48
+FIRST_K_DENSE_REPLACE=0
+
+arr=()
+for ((i=0; i<NLAYERS; i++)); do
+  if (( i < FIRST_K_DENSE_REPLACE )); then
+    arr+=(0)
+  else
+    arr+=(1)
+  fi
+done
+
+printf -v MOE_LAYER_FREQ "[%s]" "$(IFS=', '; echo "${arr[*]}")"
+
+
+MODEL_ARGS=(
+   --disable-bias-linear
+   --qk-layernorm
+   --group-query-attention
+   --num-attention-heads 32
+   --num-query-groups 4
+   --kv-channels 128
+   --num-layers 48
+   --hidden-size 2048
+   --ffn-hidden-size 6144
+
+   --normalization RMSNorm
+   --position-embedding-type rope
+   --norm-epsilon 1e-6
+   --rotary-percent 1.0
+   --swiglu
+   --untie-embeddings-and-output-weights
+   --vocab-size 151936
+
+   --rotary-base 1000000
+
+   # moe
+   --moe-ffn-hidden-size 768
+   --moe-router-score-function softmax
+   --moe-token-dispatcher-type alltoall
+   --moe-router-topk 8
+   --moe-layer-freq $MOE_LAYER_FREQ
+   --num-experts 128
+   --moe-grouped-gemm
+   --moe-token-drop-policy probs
+   --moe-router-dtype fp32
+   --moe-permute-fusion
+   --moe-aux-loss-coeff 0
+)

configs/models/qwen3-4B.sh

@@ -0,0 +1,20 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+MODEL_ARGS=(
+   --swiglu
+   --num-layers 36
+   --hidden-size 2560
+   --ffn-hidden-size 9728
+   --num-attention-heads 32
+   --group-query-attention
+   --num-query-groups 8
+   --use-rotary-position-embeddings
+   --disable-bias-linear
+   --normalization "RMSNorm"
+   --norm-epsilon 1e-6
+   --rotary-base "${MODEL_ARGS_ROTARY_BASE:-1000000}"
+   --vocab-size 151936
+   --kv-channels 128
+   --qk-layernorm
+)

configs/models/qwen3-8B.sh

@@ -0,0 +1,21 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+MODEL_ARGS=(
+   --swiglu
+   --num-layers 36
+   --hidden-size 4096
+   --ffn-hidden-size 12288
+   --num-attention-heads 32
+   --group-query-attention
+   --num-query-groups 8
+   --use-rotary-position-embeddings
+   --disable-bias-linear
+   --normalization "RMSNorm"
+   --norm-epsilon 1e-6
+   --rotary-base 1000000
+   --vocab-size 151936
+   --kv-channels 128
+   --qk-layernorm
+   --untie-embeddings-and-output-weights
+)

configs/opd/qwen3-4b-opd.py

@@ -0,0 +1,190 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+from pathlib import Path
+
+import slime.utils.external_utils.command_utils as U
+from slime.utils.external_utils.command_utils import get_bool_env_var
+
+# Standard OPD: requires a live teacher server during training.
+# 2 GPUs for actor (TP=2), 4 GPUs for rollout, 2 GPUs for teacher server.
+#
+# Required env vars:
+#   SFT_CHECKPOINT - path to the SFT checkpoint (HF format)
+
+MODEL_NAME = "Qwen3-4B-Base-Open-Thoughts-Qwen3-8B-sft-3k"
+TEACHER_MODEL_NAME = "Qwen3-8B"
+TEACHER_IP = os.environ.get("MASTER_ADDR", "127.0.0.1")
+TEACHER_PORT = 13141
+MODEL_TYPE = "qwen3-4B"
+NUM_GPUS = 8
+SFT_CHECKPOINT = os.environ["SFT_CHECKPOINT"]
+
+
+def deploy_teacher_model():
+    import random, string
+    random_suffix = ''.join(random.choices(string.ascii_letters + string.digits, k=6))
+    LOG_FILE = f"/tmp/sglang_{random_suffix}.log"
+    external_ray = get_bool_env_var("SLIME_SCRIPT_EXTERNAL_RAY")
+
+    U.exec_command(
+        "pkill -9 sglang; "
+        "sleep 3; "
+        f"{'' if external_ray else 'ray stop --force; '}"
+        f"{'' if external_ray else 'pkill -9 ray; '}"
+        "pkill -9 slime; "
+        "sleep 3; "
+        f"{'' if external_ray else 'pkill -9 ray; '}"
+        "pkill -9 slime; "
+        "pkill -9 redis; "
+        "true;"
+    )
+
+    U.exec_command(
+        f"CUDA_VISIBLE_DEVICES=6,7 python3 -m sglang.launch_server "
+        f"--model-path /root/models/{TEACHER_MODEL_NAME} "
+        f"--host 0.0.0.0 "
+        f"--port {TEACHER_PORT} "
+        f"--tp 2 "
+        f"--chunked-prefill-size 4096 "
+        f"--mem-fraction-static 0.6 "
+        f"--context-length 32768 "
+        f"> {LOG_FILE} 2>&1 & "
+    )
+
+    U.exec_command(
+        f"until curl -sf http://{TEACHER_IP}:{TEACHER_PORT}/health_generate > /dev/null; do "
+        f"    echo 'Waiting for teacher model...'; "
+        f"    tail -n 10 {LOG_FILE}; sleep 5; done; "
+        f"echo 'Teacher model ready at {TEACHER_IP}:{TEACHER_PORT}.'; sleep 10;"
+    )
+
+
+def prepare():
+    U.exec_command("mkdir -p /root/models /root/datasets")
+    U.exec_command(f"huggingface-cli download Qwen/{TEACHER_MODEL_NAME} --local-dir /root/models/{TEACHER_MODEL_NAME}")
+
+    U.convert_checkpoint(
+        model_name=MODEL_NAME,
+        megatron_model_type=MODEL_TYPE,
+        num_gpus_per_node=NUM_GPUS,
+        hf_checkpoint=SFT_CHECKPOINT,
+    )
+
+    deploy_teacher_model()
+
+
+def execute(rerun=True):
+    load_save_path = f"/root/models/{MODEL_NAME}_ckpt__{Path(__file__).stem}/"
+
+    ckpt_args = (
+        f"--hf-checkpoint {SFT_CHECKPOINT} "
+        f"--ref-load /root/models/{MODEL_NAME}_torch_dist "
+        f"--load {load_save_path} "
+        f"--save {load_save_path} "
+        "--save-interval 10 "
+        "--save-retain-interval 10 "
+    )
+
+    rollout_args = (
+        "--prompt-data /root/datasets/dapo-math-17k/dapo-math-17k.jsonl "
+        "--input-key prompt "
+        "--label-key label "
+        "--apply-chat-template "
+        "--rollout-shuffle "
+        "--num-rollout 3000 "
+        "--rollout-batch-size 64 "
+        "--n-samples-per-prompt 4 "
+        "--rollout-max-response-len 4096 "
+        "--rollout-temperature 0.8 "
+        "--global-batch-size 256 "
+        "--balance-data "
+    )
+
+    rm_args = (
+        "--custom-rm-path slime.rollout.on_policy_distillation.reward_func "
+        "--custom-reward-post-process-path slime.rollout.on_policy_distillation.post_process_rewards "
+        f"--rm-url http://{TEACHER_IP}:{TEACHER_PORT}/generate "
+        "--include-verifiable-reward "
+    )
+
+    perf_args = (
+        "--tensor-model-parallel-size 2 "
+        "--sequence-parallel "
+        "--pipeline-model-parallel-size 1 "
+        "--context-parallel-size 1 "
+        "--expert-model-parallel-size 1 "
+        "--expert-tensor-parallel-size 1 "
+        "--recompute-granularity full "
+        "--recompute-method uniform "
+        "--recompute-num-layers 1 "
+        "--use-dynamic-batch-size "
+        "--max-tokens-per-gpu 16384 "
+    )
+
+    grpo_args = (
+        "--advantage-estimator on_policy_distillation "
+        "--use-kl-loss "
+        "--kl-loss-coef 0.00 "
+        "--kl-loss-type low_var_kl "
+        "--entropy-coef 0.00 "
+    )
+
+    optimizer_args = (
+        "--optimizer adam "
+        "--lr 2e-6 "
+        "--lr-decay-style constant "
+        "--weight-decay 0.1 "
+        "--adam-beta1 0.9 "
+        "--adam-beta2 0.98 "
+    )
+
+    wandb_args = ""
+    if os.environ.get("WANDB_KEY"):
+        wandb_args = (
+            "--use-wandb "
+            "--wandb-project lightning-opd "
+            f"--wandb-group {Path(__file__).stem} "
+            f"--wandb-key {os.environ['WANDB_KEY']} "
+        )
+
+    sglang_args = (
+        "--rollout-num-gpus-per-engine 1 "
+        "--sglang-mem-fraction-static 0.4 "
+    )
+
+    misc_args = (
+        "--attention-dropout 0.0 "
+        "--hidden-dropout 0.0 "
+        "--accumulate-allreduce-grads-in-fp32 "
+        "--attention-softmax-in-fp32 "
+        "--attention-backend flash "
+        "--actor-num-nodes 1 "
+        "--actor-num-gpus-per-node 2 "
+        "--rollout-num-gpus 4 "
+    )
+
+    train_args = (
+        f"{ckpt_args} "
+        f"{rollout_args} "
+        f"{rm_args} "
+        f"{grpo_args} "
+        f"{optimizer_args} "
+        f"{wandb_args} "
+        f"{perf_args} "
+        f"{sglang_args} "
+        f"{misc_args} "
+    )
+
+    U.execute_train(
+        rerun=rerun,
+        train_args=train_args,
+        num_gpus_per_node=NUM_GPUS,
+        megatron_model_type=MODEL_TYPE,
+    )
+
+
+if __name__ == "__main__":
+    prepare()
+    execute(rerun=False)

configs/opd/qwen3-8b-opd.py

@@ -0,0 +1,190 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+from pathlib import Path
+
+import slime.utils.external_utils.command_utils as U
+from slime.utils.external_utils.command_utils import get_bool_env_var
+
+# Standard OPD: requires a live teacher server during training.
+# 4 GPUs for actor (TP=4), 2 GPUs for rollout, 2 GPUs for teacher server.
+#
+# Required env vars:
+#   SFT_CHECKPOINT - path to the SFT checkpoint (HF format)
+
+MODEL_NAME = "Qwen3-8B-Base-Open-Thoughts-Qwen3-32B-sft-3.5k"
+TEACHER_MODEL_NAME = "Qwen3-32B"
+TEACHER_IP = os.environ.get("MASTER_ADDR", "127.0.0.1")
+TEACHER_PORT = 13141
+MODEL_TYPE = "qwen3-8B"
+NUM_GPUS = 8
+SFT_CHECKPOINT = os.environ["SFT_CHECKPOINT"]
+
+
+def deploy_teacher_model():
+    import random, string
+    random_suffix = ''.join(random.choices(string.ascii_letters + string.digits, k=6))
+    LOG_FILE = f"/tmp/sglang_{random_suffix}.log"
+    external_ray = get_bool_env_var("SLIME_SCRIPT_EXTERNAL_RAY")
+
+    U.exec_command(
+        "pkill -9 sglang; "
+        "sleep 3; "
+        f"{'' if external_ray else 'ray stop --force; '}"
+        f"{'' if external_ray else 'pkill -9 ray; '}"
+        "pkill -9 slime; "
+        "sleep 3; "
+        f"{'' if external_ray else 'pkill -9 ray; '}"
+        "pkill -9 slime; "
+        "pkill -9 redis; "
+        "true;"
+    )
+
+    U.exec_command(
+        f"CUDA_VISIBLE_DEVICES=6,7 python3 -m sglang.launch_server "
+        f"--model-path /root/models/{TEACHER_MODEL_NAME} "
+        f"--host 0.0.0.0 "
+        f"--port {TEACHER_PORT} "
+        f"--tp 2 "
+        f"--chunked-prefill-size 4096 "
+        f"--mem-fraction-static 0.6 "
+        f"--context-length 32768 "
+        f"> {LOG_FILE} 2>&1 & "
+    )
+
+    U.exec_command(
+        f"until curl -sf http://{TEACHER_IP}:{TEACHER_PORT}/health_generate > /dev/null; do "
+        f"    echo 'Waiting for teacher model...'; "
+        f"    tail -n 10 {LOG_FILE}; sleep 5; done; "
+        f"echo 'Teacher model ready at {TEACHER_IP}:{TEACHER_PORT}.'; sleep 10;"
+    )
+
+
+def prepare():
+    U.exec_command("mkdir -p /root/models /root/datasets/dapo-math-17k")
+    U.exec_command(f"huggingface-cli download Qwen/{TEACHER_MODEL_NAME} --local-dir /root/models/{TEACHER_MODEL_NAME}")
+
+    U.convert_checkpoint(
+        model_name=MODEL_NAME,
+        megatron_model_type=MODEL_TYPE,
+        num_gpus_per_node=NUM_GPUS,
+        hf_checkpoint=SFT_CHECKPOINT,
+    )
+
+    deploy_teacher_model()
+
+
+def execute(rerun=True):
+    load_save_path = f"/root/models/{MODEL_NAME}_ckpt__{Path(__file__).stem}/"
+
+    ckpt_args = (
+        f"--hf-checkpoint {SFT_CHECKPOINT} "
+        f"--ref-load /root/models/{MODEL_NAME}_torch_dist "
+        f"--load {load_save_path} "
+        f"--save {load_save_path} "
+        "--save-interval 5 "
+        "--save-retain-interval 5 "
+    )
+
+    rollout_args = (
+        "--prompt-data /root/datasets/dapo-math-17k/dapo-math-17k.jsonl "
+        "--input-key prompt "
+        "--label-key label "
+        "--apply-chat-template "
+        "--rollout-shuffle "
+        "--num-rollout 3000 "
+        "--rollout-batch-size 64 "
+        "--n-samples-per-prompt 4 "
+        "--rollout-max-response-len 4096 "
+        "--rollout-temperature 0.8 "
+        "--global-batch-size 256 "
+        "--balance-data "
+    )
+
+    rm_args = (
+        "--custom-rm-path slime.rollout.on_policy_distillation.reward_func "
+        "--custom-reward-post-process-path slime.rollout.on_policy_distillation.post_process_rewards "
+        f"--rm-url http://{TEACHER_IP}:{TEACHER_PORT}/generate "
+        "--include-verifiable-reward "
+    )
+
+    perf_args = (
+        "--tensor-model-parallel-size 4 "
+        "--sequence-parallel "
+        "--pipeline-model-parallel-size 1 "
+        "--context-parallel-size 1 "
+        "--expert-model-parallel-size 1 "
+        "--expert-tensor-parallel-size 1 "
+        "--recompute-granularity full "
+        "--recompute-method uniform "
+        "--recompute-num-layers 1 "
+        "--use-dynamic-batch-size "
+        "--max-tokens-per-gpu 8192 "
+    )
+
+    grpo_args = (
+        "--advantage-estimator on_policy_distillation "
+        "--use-kl-loss "
+        "--kl-loss-coef 0.00 "
+        "--kl-loss-type low_var_kl "
+        "--entropy-coef 0.00 "
+    )
+
+    optimizer_args = (
+        "--optimizer adam "
+        "--lr 2e-6 "
+        "--lr-decay-style constant "
+        "--weight-decay 0.1 "
+        "--adam-beta1 0.9 "
+        "--adam-beta2 0.98 "
+    )
+
+    wandb_args = ""
+    if os.environ.get("WANDB_KEY"):
+        wandb_args = (
+            "--use-wandb "
+            "--wandb-project lightning-opd "
+            f"--wandb-group {Path(__file__).stem} "
+            f"--wandb-key {os.environ['WANDB_KEY']} "
+        )
+
+    sglang_args = (
+        "--rollout-num-gpus-per-engine 1 "
+        "--sglang-mem-fraction-static 0.4 "
+    )
+
+    misc_args = (
+        "--attention-dropout 0.0 "
+        "--hidden-dropout 0.0 "
+        "--accumulate-allreduce-grads-in-fp32 "
+        "--attention-softmax-in-fp32 "
+        "--attention-backend flash "
+        "--actor-num-nodes 1 "
+        "--actor-num-gpus-per-node 4 "
+        "--rollout-num-gpus 2 "
+    )
+
+    train_args = (
+        f"{ckpt_args} "
+        f"{rollout_args} "
+        f"{rm_args} "
+        f"{grpo_args} "
+        f"{optimizer_args} "
+        f"{wandb_args} "
+        f"{perf_args} "
+        f"{sglang_args} "
+        f"{misc_args} "
+    )
+
+    U.execute_train(
+        rerun=rerun,
+        train_args=train_args,
+        num_gpus_per_node=NUM_GPUS,
+        megatron_model_type=MODEL_TYPE,
+    )
+
+
+if __name__ == "__main__":
+    prepare()
+    execute(rerun=False)

configs/sft/data/sft_data/openthoughts3_300k_qwen3-8b.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66cf6cffd11dd523d28cd449afcd5500c62ab47e6617a977039e931d22ae2689
+size 13944494336

configs/sft/dataset_info.json

@@ -0,0 +1,23 @@
+{
+  "openthoughts3_300k_qwen3-8b": {
+    "file_name": "data/sft_data/openthoughts3_300k_qwen3-8b.jsonl",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant",
+      "system_tag": "system"
+    }
+  },
+  "openthoughts3_300k_qwen3-32b": {
+    "file_name": "data/sft_data/openthoughts3_300k_qwen3-32b.parquet",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages"
+    }
+  }
+}

configs/sft/dataset_info_ori_Fri_5_Jun.json

@@ -0,0 +1,16 @@
+{
+  "openthoughts3_300k_qwen3-8b": {
+    "file_name": "data/sft_data/openthoughts3_300k_qwen3-8b.jsonl",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages"
+    }
+  },
+  "openthoughts3_300k_qwen3-32b": {
+    "file_name": "data/sft_data/openthoughts3_300k_qwen3-32b.parquet",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages"
+    }
+  }
+}

configs/sft/qwen3-4b-base-open-thoughts3-qwen3-8b.yaml

@@ -0,0 +1,42 @@
+### model
+model_name_or_path: model_weights/qwen3-4b-base
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+deepspeed: examples/deepspeed/ds_z0_config.json 
+enable_liger_kernel: true
+packing: true
+
+
+### dataset
+dataset: openthoughts3_300k_qwen3-8b
+template: qwen3
+cutoff_len: 16384
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_persistent_workers: true
+dataloader_pin_memory: true
+dataloader_num_workers: 4
+
+### output
+logging_steps: 1
+save_steps: 100
+save_total_limit: 10
+plot_loss: true
+overwrite_output_dir: false
+save_only_model: false
+#report_to: wandb
+report_to: none
+run_name: qwen3-4b-base-open-thoughts3-qwen3-8b
+
+### train
+per_device_train_batch_size: 4
+gradient_accumulation_steps: 2
+learning_rate: 0.00008
+max_steps: 3000
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000

configs/sft/qwen3-8b-base-open-thoughts3-qwen3-32b.yaml

@@ -0,0 +1,41 @@
+### model
+model_name_or_path: Qwen/Qwen3-8B-Base
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+deepspeed: examples/deepspeed/ds_z1_config.json 
+enable_liger_kernel: true
+packing: true
+
+
+### dataset
+dataset: openthoughts3_300k_qwen3-32b
+template: qwen3
+cutoff_len: 16384
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_persistent_workers: true
+dataloader_pin_memory: true
+dataloader_num_workers: 4
+
+### output
+logging_steps: 1
+save_steps: 100
+save_total_limit: 10
+plot_loss: true
+overwrite_output_dir: false
+save_only_model: false
+report_to: wandb
+run_name: qwen3-8b-base-open-thoughts3-qwen3-32b
+
+### train
+per_device_train_batch_size: 2
+gradient_accumulation_steps: 2
+learning_rate: 8e-5
+max_steps: 3000
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000

configs/sft/run_sft.sh

@@ -0,0 +1,49 @@
+#!/usr/bin/env bash
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Step 2: Run SFT training with LlamaFactory.
+#
+# Required environment variables:
+#   CONFIG_YAML - Name of the SFT config file in configs/sft/ (e.g. qwen3-4b-base-sft-qwen3-8b.yaml)
+#   OUTPUT_DIR  - Directory for the SFT checkpoint output
+#
+# Optional:
+#   NUM_NODES   - Number of nodes (default: 4)
+#   NUM_GPUS    - GPUs per node (default: 8)
+#   MASTER_ADDR - Master node address (default: localhost)
+#
+# Prerequisites:
+#   - LlamaFactory installed (pip install llamafactory)
+#   - SFT data generated by Step 1 and registered in LlamaFactory's dataset_info.json
+
+set -euo pipefail
+
+: "${CONFIG_YAML:?Set CONFIG_YAML (e.g. qwen3-4b-base-sft-qwen3-8b.yaml)}"
+: "${OUTPUT_DIR:?Set OUTPUT_DIR for SFT checkpoint output}"
+
+NUM_NODES="${NUM_NODES:-4}"
+NUM_GPUS="${NUM_GPUS:-8}"
+MASTER_ADDR="${MASTER_ADDR:-localhost}"
+MASTER_PORT="${MASTER_PORT:-29500}"
+
+# torchrun \
+#     --nnodes "${NUM_NODES}" \
+#     --nproc_per_node="${NUM_GPUS}" \
+#     --rdzv_id $RANDOM \
+#     --rdzv_backend c10d \
+#     --rdzv_endpoint "${MASTER_ADDR}:29500" \
+#     -m llamafactory.cli.train \
+#     "configs/sft/${CONFIG_YAML}" \
+#     "dataset_dir=configs/sft" \
+#     "output_dir=${OUTPUT_DIR}"
+
+FORCE_TORCHRUN=1 \
+NNODES="${NUM_NODES}" \
+NPROC_PER_NODE="${NUM_GPUS}" \
+MASTER_ADDR="${MASTER_ADDR}" \
+MASTER_PORT="${MASTER_PORT}" \
+llamafactory-cli train \
+    "configs/sft/${CONFIG_YAML}" \
+    "dataset_dir=configs/sft" \
+    "output_dir=${OUTPUT_DIR}"