---

name: training-workflows description: "Construct, audit, and adapt VLM-R1 GRPO training launches for QwenVL, InternVL, LoRA, freeze-vision, DeepSpeed, and multi-node runs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

VLM-R1 training workflows

Use this sub-skill when a user needs to build, review, or modify a VLM-R1 GRPO training launch. It covers command construction, trainer/config flags, distributed launch choices, and training-specific failure diagnosis.

Route here for

• JSONL-driven GRPO launches with grpo_jsonl.py for REC, GUI or other task types.
• Qwen2-VL, Qwen2.5-VL, and InternVL command variants.
• Full fine-tuning, LoRA, freeze_vision_modules, and gradient checkpointing choices.
• DeepSpeed ZeRO-2, ZeRO-3, and ZeRO-3-offload command wiring.
• torchrun single-node and multi-node command construction.
• Training-specific CUDA, FlashAttention, DeepSpeed, W&B, data-path, and rendezvous troubleshooting.

Route elsewhere

• JSONL schema details, answer/reward formats, bbox scoring, and reward-method selection: ../data-and-rewards/SKILL.md.
• VLM module internals, adding a new model family, and Qwen/InternVL processor behavior: ../model-modules/SKILL.md.
• REC/OVD evaluation or scoring saved model outputs: ../evaluation/SKILL.md.
• Huawei Ascend inference or serving: ../ascend-inference/SKILL.md.
• LLaMA-Factory SFT: treat as an external alternative, not a VLM-R1 GRPO launch path.

Operating workflow

1. Identify the task family, model family, GPU topology, DeepSpeed mode, and logging policy.
2. Validate that data_file_paths, image_folders, and optional reward_method lists have matching colon-separated lengths.
3. Pick a baseline recipe from references/grpo-training-workflows.md and adapt only the required knobs.
4. Use scripts/launch_grpo_jsonl.sh to render or execute a checked single-node command. Default to dry-run preview before a long training job.
5. For multi-node runs, use scripts/render_multinode_torchrun.py to render one command per node and validate the host map before launch.
6. Check references/configuration-reference.md for flag semantics and trainer constraints, especially global-batch divisibility by num_generations.
7. If the launch fails before useful training starts, diagnose with references/troubleshooting.md before changing model code or data.

Bundled files

• references/grpo-training-workflows.md - recipes and adaptation patterns.
• references/configuration-reference.md - distilled CLI/trainer/DeepSpeed/torchrun flag reference.
• references/troubleshooting.md - training-specific failure triage.
• scripts/launch_grpo_jsonl.sh - safe parameterized GRPO command renderer/launcher.
• scripts/render_multinode_torchrun.py - validated multi-node command renderer.

Safety defaults

• Prefer command rendering and static validation before launching full training.
• Do not assume datasets, checkpoints, W&B credentials, or multi-node rendezvous are present.
• Do not claim full native training verification from script syntax checks alone.
• Keep user-provided paths out of reusable notes unless they are rewritten as placeholders or parameters.