---
base_model:
- Qwen/Qwen3-VL-4B-Instruct
language:
- en
license: apache-2.0
pipeline_tag: image-to-image
library_name: transformers
tags:
- autonomous-driving
- vision-language-action
- chain-of-thought
- trajectory-prediction
- VLA
---

# OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation

**[📄 Paper (arXiv)](https://arxiv.org/abs/2604.18486)** | **[💻 GitHub](https://github.com/xiaomi-research/onevl)** | **[🌐 Project Page](https://Xiaomi-Embodied-Intelligence.github.io/OneVL/)**

OneVL is a Vision-Language-Action (VLA) framework for autonomous driving that achieves state-of-the-art trajectory prediction accuracy while matching the inference latency of answer-only autoregressive models.

## Overview

Prior latent Chain-of-Thought (CoT) methods compress reasoning into opaque hidden states — fast, but consistently underperform explicit CoT on driving tasks. OneVL identifies the root cause: purely linguistic latents encode abstract semantic labels rather than the spatiotemporal causal dynamics that govern real driving scenes. OneVL addresses this with **dual-modal auxiliary decoders** that force compact latent tokens to encode both human-readable reasoning *and* future scene dynamics simultaneously.

At inference, both decoders are discarded and all latents are **prefilled** into the prompt context in a single parallel pass — matching answer-only AR prediction speed while recovering the interpretability of explicit CoT in both vision and language.

## Architecture

OneVL augments **Qwen3-VL-4B-Instruct** with:

- **Latent Token Interface**: 4 visual latent tokens + 2 language latent tokens inserted in the assistant response before the answer.
- **Visual Auxiliary Decoder**: Predicts future-frame visual tokens at t+0.5s and t+1.0s from visual latent hidden states (using the Emu3.5 IBQ codebook), acting as a world model.
- **Language Auxiliary Decoder**: Reconstructs explicit CoT reasoning text from language latent hidden states.
- **Prefill Inference**: Both decoders are discarded at inference; latent tokens are processed in one parallel pass with only the trajectory generated autoregressively.

## Results

OneVL is the first latent CoT method to surpass explicit autoregressive CoT across major driving benchmarks.

### NAVSIM
| Method | Model Size | PDM-score ↑ | Latency (s) ↓ | Interpretability |
|---|:---:|:---:|:---:|:---:|
| AR CoT+Answer | 4B | 88.29 | 6.58 | Language |
| **OneVL** | **4B** | **88.84** | **4.46** | **Vision + Language** |

### ROADWork
| Method | ADE (px) ↓ | FDE (px) ↓ | Latency (s) ↓ |
|---|:---:|:---:|:---:|
| AR CoT+Answer | 13.18 | 29.98 | 10.74 |
| **OneVL** | **12.49** | **28.80** | **4.71** |

## Usage

### Requirements
- `transformers >= 4.57.0` (required for `Qwen3VLForConditionalGeneration`)

### Inference (Trajectory Prediction Only)
```bash
python infer_onevl.py \
    --model_path /path/to/OneVL-checkpoint \
    --test_set_path test_data/navsim_test.json \
    --image_base_path "" \
    --output_path output/navsim/results.json \
    --device cuda:0 \
    --num_latent 2 --num_latent_vis 4 \
    --max_new_tokens 1024 --answer_prefix "[" --prefix_k 0
```

### Inference with Language + Visual Explanation
```bash
python infer_onevl.py \
    --model_path /path/to/OneVL-checkpoint \
    --test_set_path test_data/navsim_test.json \
    --image_base_path "" \
    --output_path output/navsim/results_explain.json \
    --device cuda:0 \
    --num_latent 2 --num_latent_vis 4 \
    --max_new_tokens 1024 --answer_prefix "[" --prefix_k 0 \
    --decoder_explain --aux_visual_condition \
    --c_thought 2 --max_explain_tokens 1024 \
    --visual_decoder_explain --visual_aux_visual_condition \
    --c_thought_visual 4 --max_visual_tokens 2560
```

## Citation

```bibtex
@article{lu2026onevl,
  title={OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation},
  author={Lu, Jinghui and Guan, Jiayi and Huang, Zhijian and Li, Jinlong and Li, Guang and Kong, Lingdong and Li, Yingyan and Wang, Han and Xu, Shaoqing and Luo, Yuechen and others},
  journal={arXiv preprint arXiv:2604.18486},
  year={2026},
  url={https://arxiv.org/abs/2604.18486}
}
```

## License
Released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
Model weights are built on [Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) and the visual tokenizer is from [Emu3.5-VisionTokenizer](https://huggingface.co/BAAI/Emu3.5-VisionTokenizer).