# FP8 RL in verl

Last updated: 03/05/2026

verl supports two FP8 modes for accelerating RL training:

| Mode | Training Precision | Rollout Precision |
|------|-------------------|-------------------|
| **FP8 Rollout Only** | BF16 | FP8 |
| **FP8 End-to-End** | FP8 (Megatron) | FP8 (vLLM) |

> [!TIP]
> For ready-to-run scripts, see the [low-precision recipe directory](https://github.com/verl-project/verl-recipe/low_precision).

---

## FP8 Rollout Only

FP8 rollout-only mode keeps training in BF16 and quantizes rollout inference to FP8. This reduces GPU memory during generation and speeds up rollout without affecting training precision.

### Implementation

We monkey patch several vLLM functions to enable FP8 rollout for reinforcement learning:

1. **Quantize weights**: Quantize model weights on-the-fly from higher-precision formats to FP8.
2. **Process weights after loading**: For vLLM, we replace the `vllm.model_executor.layers.quantization.fp8.Fp8LinearMethod.process_weights_after_loading` function to handle weight processing after quantization. For SGLang, this patch is not needed as it natively supports loading quantized weights.

### Support Matrix

- FP8 blockwise quantization for rollout
  - Used in Deepseek, which is 1x128 quantization for activations and 128x128 quantization for model weights
- Dense models and MoE models
- Async rollout interfaces
- vLLM 0.10.x & vLLM 0.11 & vLLM 0.12 & SGLang 0.5.5
- FSDP and Megatron training backends

### Usage

Enable in config file:

```yaml
rollout:
  quantization: "fp8"
```

Or via command line:

```bash
actor_rollout_ref.rollout.quantization=fp8
```

### Experiments and Outcomes

#### Qwen3-8B-Base Dense Model

**Configuration**
- DAPO recipe. AIME24 online validation.
- vLLM(FP8 spmd rollout) + FSDP
  - Note that SPMD rollout has been deprecated, so we removed the FP8 SPMD rollout.
- Prompt batch size 32, n=16.
- Rollout batch size: 32\*3*16
- Train_batch_size & ppo_mini_batch_size 32
- Max response length 20K
- Token-level TIS, C=2
- 8*H100
- vLLM 0.10.0+CUDA 12.6 vs vLLM 0.11.0+CUDA 12.9

**Accuracy**
![Qwen3-8b-base_fp8_acc](
https://github.com/Agoniii/verl/blob/xueh/fp8_pr_images/docs/advance/images/Qwen3-8b-base_fp8_acc.png?raw=true)
*dark green: BF16, orange: FP8 rollout + token-level TIS, light green: FP8 rollout without TIS*

Results and observations:
- With TIS, FP8 rollout aligns with BF16
- Obvious accuracy drop when TIS is not enabled
- Higher mismatch kl but within acceptable range throughout the training


**Performance**

![Qwen3-8b-base_fp8_rollout_perf](
https://github.com/Agoniii/verl/blob/xueh/fp8_pr_images/docs/advance/images/Qwen3-8b-base_fp8_rollout_perf.png?raw=true)
*green: BF16, orange: FP8 rollout + CUDA12.6 + DeepGemm, purple: FP8 rollout + CUDA 12.9 + DeepGemm*

Results and observations:
- FP8 rollout leads to around ~12% rollout speedup with CUDA 12.6 + DeepGemm
- When upgrading to CUDA 12.9, speedup can be up to ~18%

#### Qwen3-30B-A3B-Base MoE Model

**Configuration**
- DAPO recipe. AIME24 online validation.
- FP8 async rollout, vLLM+FSDP
- Prompt batch size 32
- Rollout batch size: 32\*3*16
- Train_batch_size & ppo_mini_batch_size 32
- Max response length 20K
- Token-level TIS, C=2
- 2\*8*H100
- vLLM 0.10.0+CUDA 12.6

**Accuracy**
![Qwen3-30b-a3b_fp8_acc](
https://github.com/Agoniii/verl/blob/xueh/fp8_pr_images/docs/advance/images/Qwen3-30b-a3b_fp8_acc.png?raw=true)
*grey: BF16 + token-level TIS, red: FP8 rollout + token-level TIS*

Results and observations:
- Rollout & training distribution mismatch is in general higher for MoE
- Rollout correction required even for BF16
- FP8 rollout with token-level TIS aligns with BF16


**Performance**

![Qwen3-30b-a3b_fp8_perf](
https://github.com/Agoniii/verl/blob/xueh/fp8_pr_images/docs/advance/images/Qwen3-30b-a3b_fp8_perf.png?raw=true)
*grey: BF16 + token-level TIS, red: FP8 rollout + token-level TIS​*

Results and observations:
- FP8 rollout : over 35% rollout speedup
- Expecting more perf gain with CUDA 12.9

---

## FP8 End-to-End (Training + Rollout)

FP8 E2E applies FP8 to the entire RL pipeline: forward/backward passes via Transformer Engine, FP8 optimizer states, and FP8 rollout inference via vLLM. This maximizes memory savings and throughput.

### Requirements

- **CUDA 12.9+** (required for block-wise FP8 scaling)
- **Transformer Engine** with block-wise FP8 support
- Environment variable: `NVTE_FP8_BLOCK_SCALING_FP32_SCALES=1`

### Key Configuration

```yaml
# FP8 training via Transformer Engine
actor_rollout_ref.actor.megatron.override_transformer_config:
  fp8: "hybrid"              # FP8 forward + backward; also supports "e4m3"
  fp8_recipe: "blockwise"    # block-wise scaling

# FP8 optimizer
actor_rollout_ref.actor.optim.override_optimizer_config:
  fp8_recipe: "blockwise"

# FP8 rollout inference (vLLM)
actor_rollout_ref.rollout:
  quantization: fp8
```

### Support Matrix

- Megatron training backend (via Megatron-Bridge)
- Verified on Qwen3-30B-A3B and Qwen3-8B
- Block-wise FP8 scaling (`fp8_recipe: "blockwise"`)

### Experiments and Results

#### Qwen3-30B-A3B MoE Model

**Configuration**
- DAPO recipe. AIME24 online validation.
- Megatron + Megatron-Bridge, FP8 async rollout with vLLM
- MoE router in BF16 for both vLLM and Megatron-Core
- Prompt batch size 128, n=16
- Max response length 20K
- Token-level TIS, C=2
- 2\*8*H100, CUDA 12.9

![Qwen3-30b-a3b_fp8_e2e](https://github.com/user-attachments/assets/70fb1396-ec73-40d7-9a43-1d48553c0ad9)
*Orange: BF16, Green: FP8 E2E, Red: FP8 rollout + BF16 training*

Results and observations:
- FP8 E2E achieves comparable accuracy to the BF16 baseline, with the two curves closely aligned throughout training.
- The training/inference precision mismatch (measured by KL divergence) follows the ordering: FP8 rollout-only > FP8 E2E > BF16 E2E. This is expected, as FP8 E2E maintains consistent precision across both training and inference, resulting in lower distribution mismatch than the FP8 rollout-only setting where training remains in BF16.

---

## Citation

For more extensive experiments, ablation studies, and analysis on FP8 reinforcement learning, please refer to our technical report:

```bibtex
@article{qiu2026fp8rl,
  title={FP8-RL: A Practical and Stable Low-Precision Stack for LLM Reinforcement Learning},
  author={Qiu, Zhaopeng and Yu, Shuang and Zhang, Jingqi and Zhang, Shuai and Huang, Xue and Yang, Jingyi and Lai, Junjie},
  journal={arXiv preprint arXiv:2601.18150},
  year={2026},
  url={https://arxiv.org/abs/2601.18150}
}
```