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dfbcd52 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | # verl Examples
This directory hosts curated, minimal-dependency examples that drive
`verl.trainer.main_ppo` with the current Hydra API. Algorithm-specific
extensions, research baselines, and non-trivial entry points live under
`recipe/`; prefer that directory if you need a custom loss or reward beyond
what these examples show.
## Conventions
All run scripts follow the same shape:
1. Canonical filename:
```
run_<model>_<train-backend>.sh
```
- `<model>`: a single canonical size per model family. E.g.
`qwen3_8b`, `qwen3_30b_a3b`, `qwen3_235b_a22b`, `qwen3_vl_8b`,
`deepseek_v3`, `mimo_7b`, `nemotron_nano_v3`.
- `<train-backend>`: one of `fsdp`, `fsdp2`, `megatron`,
`megatron_lite`, `mindspeed`, `automodel`, or `veomni`. **Must be the
final suffix before `.sh`**.
Nothing follows `<train-backend>`. Per-example *features* — including
the inference backend (`vllm`/`sglang`/`trtllm`), the platform
(`DEVICE=gpu|npu`), the GPU machine type (`MACHINE=gb200`/`b200`/
`blackwell`), Liger kernel, LoRA, FP8 quantization, sequence parallel
size, server vs sync rollout, etc. — do **not** show up in filenames.
They are exposed as env-var toggles inside the one canonical script.
Do not add `_npu`, `_amd`, `_vllm`, `_sglang`, `_trtllm`, or `_fp8`
script variants. For example, `sft/gsm8k/run_qwen2_5_0_5b_fsdp.sh`
covers plain SFT and its `USE_LIGER=1`, `SP_SIZE=2`, `USE_PEFT=1`
variants via env vars; `grpo_trainer/run_qwen3_8b_fsdp.sh` covers vLLM,
SGLang, and TRT-LLM rollouts, CUDA/NPU platforms, and `MACHINE=gb200`
(Blackwell) via toggles.
This naming rule is enforced by the `check-example-naming` pre-commit
hook (see `tests/special_sanity/check_example_naming.py`).
2. Every script exposes its important knobs in a user-adjustable region near
the top. Derived defaults and device/backend-specific details belong below
the "no user adjustment needed below" / "derived defaults" boundary.
Use uppercase env vars for user-facing knobs, e.g.
```bash
# ---- user-adjustable ----
DEVICE=${DEVICE:-gpu}
INFER_BACKEND=${INFER_BACKEND:-vllm}
MODEL_PATH=${MODEL_PATH:-Qwen/Qwen3-8B}
NNODES=${NNODES:-1}
NDEVICES_PER_NODE=${NDEVICES_PER_NODE:-}
TRAIN_BATCH_SIZE=${TRAIN_BATCH_SIZE:-1024}
ROLLOUT_TP=${ROLLOUT_TP:-2}
ROLLOUT_N=${ROLLOUT_N:-5}
PROJECT_NAME=${PROJECT_NAME:-verl_grpo_gsm8k_math}
EXPERIMENT_NAME=${EXPERIMENT_NAME:-qwen3_8b_grpo_vllm_fsdp}
# ---- end user-adjustable ----
...
```
Override anything you care about on the command line:
```bash
DEVICE=npu MODEL_PATH=/my/local/qwen3-8b NDEVICES_PER_NODE=4 bash examples/grpo_trainer/run_qwen3_8b_fsdp.sh
```
GPU and NPU paths should share the same `PROJECT_NAME` /
`EXPERIMENT_NAME` form. Do not append `_npu` to project or experiment
names just because `DEVICE=npu` is selected.
3. Defaults (unless a directory explicitly documents otherwise):
- `data.train_files` + `data.val_files` = GSM8K + MATH for text LLMs
(`geo3k` for vision, `dapo-math-17k` / `aime-2024` for scale-demo 235B /
671B scripts).
- `actor_rollout_ref.actor.use_dynamic_bsz=True`
- `trainer.balance_batch=True`
- `trainer.logger=["console","wandb"]`.
4. No deprecated Hydra knobs:
- `ppo_megatron_trainer.yaml` → use `actor_rollout_ref.actor.model_engine=megatron`.
- `actor_rollout_ref.rollout.mode=async` → removed; async rollout is no
longer selected this way in example scripts.
- `actor_rollout_ref.hybrid_engine=True` → removed; the trainer now
enforces the supported hybrid-engine path internally.
- `ppo_micro_batch_size` / `log_prob_micro_batch_size` → use the
`_per_gpu` suffix.
- `data.val_batch_size` → removed.
- Top-level `reward_model.*` → use `reward_model.reward_model.*` /
`reward.reward_model.*` as applicable.
- `actor.ulysses_sequence_parallel_size` → use
`actor_rollout_ref.actor.fsdp_config.ulysses_sequence_parallel_size`.
## Directory layout
### Algorithm trainers
Each directory holds a canonical recipe for one training algorithm. Adding a
new algorithm? If it needs its own trainer entry point or reward code, put it
under `recipe/` instead.
| Dir | Algorithm | `algorithm.adv_estimator` / `policy_loss.loss_mode` |
|------------------------------------|--------------------------------|-----------------------------------------------------|
| `ppo_trainer/` | PPO (actor + critic) | `adv_estimator=gae` |
| `grpo_trainer/` | GRPO | `adv_estimator=grpo` |
| `rloo_trainer/` | RLOO | `adv_estimator=rloo` |
| `remax_trainer/` | ReMax | `adv_estimator=remax` |
| `reinforce_plus_plus_trainer/` | REINFORCE++ / baseline | `adv_estimator=reinforce_plus_plus[_baseline]` |
| `cispo_trainer/` | CISPO | `loss_mode=cispo` |
| `dppo_trainer/` | DPPO (TV / KL variants) | `loss_mode=dppo_tv \| dppo_kl` |
| `gdpo_trainer/` | GDPO | `adv_estimator=gdpo` |
| `gmpo_trainer/` | GMPO | `loss_mode=geo_mean` |
| `gpg_trainer/` | GPG | `adv_estimator=gpg`, `loss_mode=gpg` |
| `gspo_trainer/` | GSPO | `loss_mode=gspo` |
| `sapo_trainer/` | SAPO | `loss_mode=sapo` |
| `otb_trainer/` | OTB | `adv_estimator=optimal_token_baseline` |
| `mtp_trainer/` | DAPO + MTP (MiMo-7B) | `adv_estimator=grpo`, MTP flags |
| `on_policy_distillation_trainer/` | on-policy distillation | GRPO + distillation loss |
| `flowgrpo_trainer/` | Flow-GRPO (diffusion) | image-gen specific |
### Feature / infra
| Dir | Purpose |
|----------------------|------------------------------------------------------------------------------------------|
| `tuning/` | LoRA (`tuning/lora/`) and scaling demos (`tuning/scaling/`). |
| `profile/` | NPU profiler / torch-memory profiler runs. |
| `sft/` | Supervised fine-tuning examples. |
| `generation/` | Rollout-only inference launches. |
| `vllm_omni/` | vLLM omni backend examples. |
| `data_preprocess/` | Scripts that produce the `$HOME/data/<dataset>/*.parquet` layout the run scripts expect. |
| `prefix_grouper/` | Prefix-grouped rollout examples. |
| `rollout_correction/`| Rollout correction examples. |
| `router_replay/` | Router replay examples. |
| `tutorial/` | Tutorials and cluster launchers (`ray/`, `slurm/`, `skypilot/`, `agent_loop_get_started/`). |
### Where are the algorithm research variants?
`recipe/` — e.g. `recipe/dapo`, `recipe/prime`, `recipe/retool`,
`recipe/r1`, `recipe/spin`, `recipe/gvpo`, `recipe/flowrl`, ...
They ship their own trainer entry points and reward code.
|