feat(recipe): add qwen3-vl-8b NPU recipe with DAPO#89
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This pull request introduces a new shell script run_dapo_qwen3_vl_8b_fsdp2_npu.sh to configure and execute DAPO training for the Qwen3-VL-8B model on NPU hardware. The script defines various hyperparameters, environment variables, and model configurations. Feedback identifies critical syntax errors where spaces were incorrectly inserted into configuration keys (e.g., actor rollout_ref instead of actor_rollout_ref) and a minor formatting inconsistency regarding line continuation characters.
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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Title: Adds Qwen3-VL-8B vision-language reasoning NPU recipe with DAPO on GEO3K
Why this recipe?
verl-recipe currently lacks vision-language (VL) reasoning recipes on NPU, despite the growing importance of multimodal reasoning.
This recipe is designed as VL reasoning with verifiable rewards:
Shows how run Qwen3-VL-8B dapo on NPU
Recipe Design
Model: Qwen3-VL-8B-Instruct (full-weight, NPU A2 * 2 nnodes)
Dataset: geo3k
Results
100 steps of DAPO on 2 A2(910B3) nnodes 64GB (~53 h):
算法: DAPO
Baseline (mean@1): 0.438
Test acc best (mean@1): 0.715