---
title: "Skywork R1V — paper by Peng et al. (2025) — summary and ideas"
description: "Vision-language models describe images well but fall behind text-only reasoning LLMs on multi-step… 23 references, cited by 17 papers here."
authors: ["Yi Peng", "Peiyu Wang", "Xiaokun Wang", "Yichen Wei", "Jiangbo Pei", "Weijie Qiu", "Ai Jian", "Yunzhuo Hao", "Jiachun Pan", "Tianyidan Xie", "Li Ge", "Rongxian Zhuang", "Xuchen Song", "Yang Liu", "Yahui Zhou"]
year: 2025
venue: "arXiv:2504.05599"
source: "https://arxiv.org/abs/2504.05599"
doi: "10.48550/arxiv.2504.05599"
arxiv: "2504.05599"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/peng-2025-skywork-r1v-pioneering-multimodal/"
---
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# Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought

**Authors:** [Yi Peng](https://md.research.elamur.ai/free/en/authors/yi-peng/), [Peiyu Wang](https://md.research.elamur.ai/free/en/authors/peiyu-wang/), [Xiaokun Wang](https://md.research.elamur.ai/free/en/authors/xiaokun-wang/), [Yichen Wei](https://md.research.elamur.ai/free/en/authors/yichen-wei/), [Jiangbo Pei](https://md.research.elamur.ai/free/en/authors/jiangbo-pei/), [Weijie Qiu](https://md.research.elamur.ai/free/en/authors/weijie-qiu/), Ai Jian, [Yunzhuo Hao](https://md.research.elamur.ai/free/en/authors/yunzhuo-hao/), Jiachun Pan, [Tianyidan Xie](https://md.research.elamur.ai/free/en/authors/tianyidan-xie/), Li Ge, Rongxian Zhuang, [Xuchen Song](https://md.research.elamur.ai/free/en/authors/xuchen-song/), [Yang Liu](https://md.research.elamur.ai/free/en/authors/yang-liu/) and [Yahui Zhou](https://md.research.elamur.ai/free/en/authors/yahui-zhou/) · **Year:** 2025 · **Venue:** arXiv:2504.05599 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2504.05599) · [PDF](https://arxiv.org/pdf/2504.05599) · [DOI 10.48550/arxiv.2504.05599](https://doi.org/10.48550/arxiv.2504.05599) · [arXiv 2504.05599](https://arxiv.org/abs/2504.05599) · [OpenAlex](https://openalex.org/W4417113140)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [17](https://md.research.elamur.ai/free/en/papers/peng-2025-skywork-r1v-pioneering-multimodal/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Vision-language models describe images well but fall behind text-only reasoning LLMs on multi-step tasks such as geometry proofs. Aligning a vision backbone with a reasoning LLM usually needs large amounts of costly R1-style multimodal reasoning data. Skywork R1V, a 38B open-weight model from Skywork AI, avoids this by separating visual alignment from reasoning.

First, an MLP projector learns to connect a ViT encoder with Qwen2.5-32B-Instruct, a non-reasoning model with the same architecture, using ordinary multimodal data. The adapter is then moved onto DeepSeek-R1-distill-Qwen2.5-32B. From that point only the adapter is trained, while the encoder and LLM stay frozen. Training alternates rounds of SFT on reward-filtered and previously failed samples, followed by GRPO. Training data come from an Adaptive-Length CoT distillation pipeline that rates samples with GPT-4o and adjusts chain length to limit overthinking.

The model scores 69.0 on MMMU and 67.5 on MathVista while keeping strong text-math results. This points to an inexpensive route to multimodal reasoning.

## Key points
- **Architecture and release:** The model combines ViT, an MLP adapter and DeepSeek-R1-distill-Qwen2.5-32B. Weights are public on Hugging Face as Skywork/Skywork-R1V-38B.
- **Adapter pretraining:** The adapter goes through three SFT passes with a frozen encoder and LLM:
  - 2M samples;
  - 200K samples selected with GPT-4;
  - 40K CoT samples.

  The preliminary Qwen-based model reaches 64.0 on MMMU. After the swap to the R1-distilled LLM, with no extra tuning, it still scores 60.2.
- **Iterative SFT:** Four rounds (T=4) use a reward model scoring 0–5, with the threshold rising from 2 to 5, plus a set of earlier errors. MMMU improves from 62.5 to 65.6 across these rounds.
- **GRPO stage:** Training uses accuracy and format rewards, 8 generations per prompt and 8k-token completions. It lifts MMMU to 69.0 and makes outputs longer.
- **Benchmark results:**
  - **MMMU:** 69.0, close to GPT-4o (69.1) and above Claude 3.5 Sonnet (66.4) and InternVL2.5-38B (63.9).
  - **MathVista:** 67.5, above Claude 3.5 Sonnet (65.3) but below InternVL2.5-38B (71.9).
  - **Text math:** 72.0 on AIME 2024 and 94.0 on MATH500.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Yi Peng, Peiyu Wang, Xiaokun Wang, Yichen Wei, Jiangbo Pei, Weijie Qiu, Ai Jian, Yunzhuo Hao, Jiachun Pan, Tianyidan Xie, Li Ge, Rongxian Zhuang, Xuchen Song, Yang Liu and Yahui Zhou. 2025. Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought. arXiv:2504.05599.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{peng2025skywork,
  title = {{Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought}},
  author = {Yi Peng, Peiyu Wang, Xiaokun Wang, Yichen Wei, Jiangbo Pei, Weijie Qiu, Ai Jian, Yunzhuo Hao, Jiachun Pan, Tianyidan Xie, Li Ge, Rongxian Zhuang, Xuchen Song, Yang Liu and Yahui Zhou},
  year = {2025},
  doi = {10.48550/arxiv.2504.05599},
  eprint = {2504.05599},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2504.05599}
}
```

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## References

23 references, 18 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*