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name: training-workflows description: "Configure and launch EasyR1 distributed RL post-training jobs with OmegaConf configs, CLI overrides, Ray/FSDP/vLLM runtime assumptions, algorithms, LoRA, logging, checkpoint resume, and launch troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

EasyR1 Training Workflows

Use this sub-skill when the task is to configure, lint, explain, or launch an EasyR1 RL post-training job through python -m verl.trainer.main.

Start here

• references/training-configuration.md: Explains the EasyR1 config hierarchy, merge order, algorithm and loss knobs, Ray/FSDP/vLLM assumptions, LoRA, logging, validation, save, and resume options.
• references/example-launch-recipes.md: Provides distilled launch recipes for GRPO, DAPO, Reinforce++, GSPO, CISPO, SAPO, LoRA, VL, logging, resume, and multi-node runs.
• references/troubleshooting.md: Maps common training, Ray, CUDA, vLLM, FSDP, LoRA, online filtering, logging, and checkpoint-resume failures to fixes.
• scripts/easyr1_config_lint.py: Safely parses an EasyR1-style YAML config and reports structural, enum, batching, runtime, DAPO, and LoRA warnings without starting training.
• scripts/easyr1_command_builder.py: Builds a shell-quoted python -m verl.trainer.main config=... key=value... command from a config path and CLI overrides without executing it.

Scope boundaries

This sub-skill owns training configuration and launch decisions: OmegaConf files, CLI overrides, python -m verl.trainer.main, Ray resource assumptions, FSDP actor/ref/critic settings, vLLM rollout settings, GRPO/DAPO/Reinforce++/ReMax/RLOO/GSPO/CISPO/SAPO choices, LoRA launch cautions, logger selection, validation, save, and resume.

Route these tasks elsewhere in the EasyR1 skill graph:

• Dataset columns, prompt templates, reward-function implementation, Android GUI reward details, and reward smoke tests belong to data-and-rewards.
• DataProto, padding/unpadding, dynamic batching internals, and low-level algorithm tensor APIs belong to core-apis.
• Converting actor checkpoints to Hugging Face format or inspecting checkpoint shards belongs to checkpoint-export.

Safety contract

Static config checks and command construction are CPU-safe and do not prove that a full training run will work. Full EasyR1 training requires a CUDA-capable runtime with the EasyR1 training stack, including Ray, vLLM, flash-attn, a compatible PyTorch build, sufficient GPUs, and accessible model and dataset assets.