Training configuration
- Base model:
Qwen/Qwen3-30B-A3B-Base
- Algorithm: GRPO
- Training data: DAPO Math 17K
- Final checkpoint: step 800 of 800
- Low-precision policy training: per-token NVFP4 W4A4, all MLP layers
- Overlong policy: disabled, so this control is aligned with the no-overlong
stability-loss experiments
- Stability methods: 0 of 3 enabled (Length, Segment, and Alignment are off)
- Transformer Engine: dequantization fix based on TE 3049
- Final recorded validation accuracy:
0.44140625
The NeMo-RL Megatron distributed checkpoint was converted with Megatron Bridge
to a standard Transformers checkpoint. The exported weights are stored as 16
BF16 safetensors shards. Optimizer and dataloader state are intentionally not
included; this repository is intended for inference, evaluation, or subsequent
fine-tuning rather than exact optimizer-state training resumption.
Minimal loading example
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "shawnzzzzz/Qwen3-30B-A3B-GRPO-W4A4-NoOverlong-Step800"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
)