EasyR1

Use this repo skill when a task names EasyR1, the verl package from EasyR1, or a multimodal LLM RL/post-training workflow that looks like EasyR1: GRPO/DAPO/Reinforce++/ReMax/RLOO/GSPO/CISPO/SAPO training, Ray + FSDP + vLLM rollout, Qwen/Qwen-VL post-training, EasyR1 reward functions, EasyR1 checkpoint merging, or DataProto debugging.

This skill is self-contained operating guidance distilled from the EasyR1 repository. It does not require reopening the repository checkout. Full training still requires an EasyR1-compatible CUDA runtime with Ray, vLLM, flash-attn, model weights, datasets, and enough GPU memory.

First checks

• Read references/repo-provenance.md before deciding whether this skill is current for a checkout or whether to refresh it.
• Read references/setup-and-troubleshooting.md for install/runtime choices, Docker/container guidance, CUDA/vLLM/flash-attn limits, and common cross-cutting failures.
• Run scripts/easyr1_env_check.py when you need a safe local environment/import/backend check. It does not start Ray, download models, or run training.

Route by task

Training workflows

Load sub-skills/training-workflows/SKILL.md when the task is to configure, lint, explain, or launch python -m verl.trainer.main jobs. It covers EasyR1 config hierarchy, OmegaConf CLI overrides, algorithms and loss types, Ray/FSDP/vLLM runtime assumptions, LoRA, logging, validation, checkpoint save/resume, multi-node setup, and training troubleshooting.

Typical triggers:

• "Run EasyR1 GRPO/DAPO/GSPO/CISPO/SAPO on my dataset."
• "Build or validate an EasyR1 config."
• "Convert an example shell launch to a safer command."
• "Explain worker.rollout.tensor_parallel_size, algorithm.online_filtering, LoRA, or GPU memory settings."

Data and rewards

Load sub-skills/data-and-rewards/SKILL.md when the task is about dataset rows, prompt templates, image/video columns, reward functions, worker.reward.reward_function, batch/sequential reward modes, DAPO overlong penalties, R1-V/math/Android GUI reward patterns, or validating reward outputs.

Typical triggers:

• "Prepare a custom text/VL dataset for EasyR1."
• "Write an EasyR1 reward function with overall, accuracy, or accuracy_normalized."
• "Debug image-token mismatch, overlong prompts, or missing reward keys."

Core APIs

Load sub-skills/core-apis/SKILL.md when the task involves EasyR1 support APIs rather than a full job launch: DataProto, tensor/non-tensor batches, padding/unpadding, dynamic sequence-length batching, compute_policy_loss, advantage/KL helpers, logger/tracker helpers, or native CPU API smoke checks.

Typical triggers:

• "Debug a DataProto.union or chunk/split error."
• "Use EasyR1 core algorithm functions directly."
• "Explain dynamic batching restore order or GRPO grouped rollout assertions."

Checkpoint export

Load sub-skills/checkpoint-export/SKILL.md when the task is to inspect EasyR1 actor checkpoint directories or convert actor shards to Hugging Face format. It covers model_world_size_<N>_rank_<R>.pt shard patterns, the huggingface/ metadata directory, generation config preservation, LoRA merge requirements, upload caveats, and safe preflight inspection.

Typical triggers:

• "Merge an EasyR1 actor checkpoint to Hugging Face format."
• "Preflight a checkpoint before running the model merger."
• "Explain missing rank shards, unsupported DTensor placement, or LoRA base-model errors."

Minimal install/import shape

For users setting up EasyR1 itself, prefer the project-documented CUDA container or an equivalent Python 3.9+ environment with compatible PyTorch, Ray, vLLM, flash-attn, transformers, and model/dataset access. A minimal import check is:

python - <<'PY'
import verl
from verl.protocol import DataProto
from verl.trainer.core_algos import compute_kl
print("verl", getattr(verl, "__version__", "unknown"))
print("DataProto", DataProto.__name__)
print("compute_kl", compute_kl.__name__)
PY

If flash_attn, vllm, CUDA, or Ray imports fail, use the setup/troubleshooting reference before treating EasyR1 training as ready. CPU-safe API checks do not prove the full training runtime.

Boundaries

Do not use this skill as a generic TRL/OpenRLHF/LlamaFactory manual. Use it when EasyR1-specific config keys, scripts, dataset/reward contracts, verl APIs, or checkpoint layout matter. For ordinary supervised fine-tuning or inference workflows that EasyR1 explicitly does not provide, route to a more suitable LLM fine-tuning or serving skill.