---
title: "高效提升多模态大语言模型推理能力的级联强化学习策略 — 王玮赟 2026"
description: "Reinforcement learning is now a standard step for improving the reasoning of multimodal large language models, but each family of methods has a weakness."
authors: ["王玮赟", "蒲恒骏", "景凌林", "乔宇"]
year: 2026
venue: "自动化学报 (Acta Automatica Sinica)"
source: "https://www.aas.net.cn/cn/article/doi/10.16383/j.aas.c250515"
doi: "10.16383/j.aas.c250515"
licence: "Free access (aas.net.cn), no open licence stated — readonly; © 《自动化学报》编辑部"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/wang-2026-cascade-reinforcement-learning/"
---
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# 高效提升多模态大语言模型推理能力的级联强化学习策略

**Authors:** 王玮赟, 蒲恒骏, 景凌林, 乔宇 · **Year:** 2026 · **Venue:** 自动化学报 (Acta Automatica Sinica) · **Licence:** Free access (aas.net.cn), no open licence stated — readonly; © 《自动化学报》编辑部

**Links:** [Publisher's page](https://doi.org/10.16383/j.aas.c250515) · [PDF](https://www.aas.net.cn/cn/article/pdf/preview/10.16383/j.aas.c250515.pdf) · [DOI 10.16383/j.aas.c250515](https://doi.org/10.16383/j.aas.c250515)

## Summary
Reinforcement learning is now a standard step for improving the reasoning of multimodal large language models, but each family of methods has a weakness. Online methods such as GRPO and GSPO reach better final accuracy, but they are expensive because the policy must generate rollouts during training, and they converge slowly. Offline preference methods such as DPO and MPO are cheaper but stop at a lower ceiling. The authors ran controlled experiments on the M3CoT dataset and propose CascadeRL, which runs the two in sequence on the same training questions. First, MPO uses pre-collected trajectories, sampled from a smaller model, to quickly prune low-quality regions of the output distribution. Then GSPO refines the model with its own rollouts. CascadeRL matches or beats pure online RL at about half the training cost. Applied to the eight instruction-tuned InternVL3.5 models, it raises average accuracy on seven multimodal reasoning benchmarks by close to 8%. An analysis of log-probabilities on positive and negative samples explains why it works: RL suppresses wrong answers, while SFT unintentionally makes them more likely.

## Key points
- **Experimental setup:** The analysis uses the M3CoT train and validation splits, which contain 7,861 training items. A limited-data regime is simulated by training on only half of them.
- **Findings on data size:** With limited data, offline RL converges faster and gains more than online RL. With the full data, online RL reaches a higher ceiling. Offline-then-online training on the same questions rivals two rounds of online RL.
- **Offline stage:** MPO combines a DPO preference loss, a BCO quality loss and an LM generation loss.
- **Online stage:** GSPO is used without a KL term to a reference model. It samples 16 responses per question, and the reward gives 0.1 for correct format and 0.9 for a correct answer. GSPO was chosen for its stability on MoE models.
- **Main results:** InternVL3.5-8B-Instruct gains +6.7% and InternVL3.5-241B-A28B-Instruct gains +6.5% in average accuracy across the seven benchmarks.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) · [Natural language processing](https://md.research.elamur.ai/free/shelves/nlp/)

## Cite

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

```bibtex
@inproceedings{2026,
  title = {{高效提升多模态大语言模型推理能力的级联强化学习策略}},
  author = {王玮赟 and 蒲恒骏 and 景凌林 and 乔宇},
  booktitle = {自动化学报 (Acta Automatica Sinica)},
  year = {2026},
  doi = {10.16383/j.aas.c250515},
  url = {https://doi.org/10.16383/j.aas.c250515}
}
```

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67. 王玮赟 复旦大学博士研究生. 2022 年获得哈尔滨工业大学计算机科学与 技术专业学士学位. 主要研究方向为 多模态大语言模型. E-mail: wywang22@m.fudan.edu.cn (WANG Wei-Yun Ph.D. candidate at Fudan University. He received his bachelor degree in computer science and technology from Harbin Institute of Technology in 2022. His main research interest is multimodal… [find](https://scholar.google.com/scholar?q=%E7%8E%8B%E7%8E%AE%E8%B5%9F+%E5%A4%8D%E6%97%A6%E5%A4%A7%E5%AD%A6%E5%8D%9A%E5%A3%AB%E7%A0%94%E7%A9%B6%E7%94%9F%2E+2022+%E5%B9%B4%E8%8E%B7%E5%BE%97%E5%93%88%E5%B0%94%E6%BB%A8%E5%B7%A5%E4%B8%9A%E5%A4%A7%E5%AD%A6%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%A7%91%E5%AD%A6%E4%B8%8E+%E6%8A%80%E6%9C%AF%E4%B8%93%E4%B8%9A%E5%AD%A6%E5%A3%AB%E5%AD%A6%E4%BD%8D%2E+%E4%B8%BB%E8%A6%81%E7%A0%94%E7%A9%B6%E6%96%B9%E5%90%91%E4%B8%BA+%E5%A4%9A%E6%A8%A1%E6%80%81%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%2E+E%2Dmail%3A+wywang22%40m%2Efudan%2Eedu%2Ecn+%28WANG+Wei%2DYun+Ph%2ED%2E+candidate+at+Fudan+University%2E+He+received+his+bachelor+degree+in+computer+science+)
68. 蒲恒骏 上海人工智能实验室见习研 究员. 2024 年获得清华大学软件工程 专业学士学位. 主要研究方向为多模 态大语言模型. E-mail: puhengjun@pjlab.org.cn (PU Heng-Jun Research intern at Shanghai Artificial Intelligence Laboratory. He received his bachelor degree in software engineering from Tsinghua University in 2024. [find](https://scholar.google.com/scholar?q=%E8%92%B2%E6%81%92%E9%AA%8F+%E4%B8%8A%E6%B5%B7%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E5%AE%9E%E9%AA%8C%E5%AE%A4%E8%A7%81%E4%B9%A0%E7%A0%94+%E7%A9%B6%E5%91%98%2E+2024+%E5%B9%B4%E8%8E%B7%E5%BE%97%E6%B8%85%E5%8D%8E%E5%A4%A7%E5%AD%A6%E8%BD%AF%E4%BB%B6%E5%B7%A5%E7%A8%8B+%E4%B8%93%E4%B8%9A%E5%AD%A6%E5%A3%AB%E5%AD%A6%E4%BD%8D%2E+%E4%B8%BB%E8%A6%81%E7%A0%94%E7%A9%B6%E6%96%B9%E5%90%91%E4%B8%BA%E5%A4%9A%E6%A8%A1+%E6%80%81%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%2E+E%2Dmail%3A+puhengjun%40pjlab%2Eorg%2Ecn+%28PU+Heng%2DJun+Research+intern+at+Shanghai+Artificial+Intelligence+Laboratory%2E+He+received+his+bachelor+deg)
69. His main research interest is multimodal large language models.) 景凌林 上海人工智能实验室青年研 究员. 2025 年获得英国拉夫堡大学计 算机科学与技术专业博士学位. 主要 研究方向为视频理解, 多模态大语言 模型. E-mail: jinglinglin@pjlab.org.cn (JING Ling-Lin Young researcher at Shanghai Artificial Intelligence Laboratory. He received his Ph.D. degree in computer sci… [find](https://scholar.google.com/scholar?q=His+main+research+interest+is+multimodal+large+language+models%2E%29+%E6%99%AF%E5%87%8C%E6%9E%97+%E4%B8%8A%E6%B5%B7%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E5%AE%9E%E9%AA%8C%E5%AE%A4%E9%9D%92%E5%B9%B4%E7%A0%94+%E7%A9%B6%E5%91%98%2E+2025+%E5%B9%B4%E8%8E%B7%E5%BE%97%E8%8B%B1%E5%9B%BD%E6%8B%89%E5%A4%AB%E5%A0%A1%E5%A4%A7%E5%AD%A6%E8%AE%A1+%E7%AE%97%E6%9C%BA%E7%A7%91%E5%AD%A6%E4%B8%8E%E6%8A%80%E6%9C%AF%E4%B8%93%E4%B8%9A%E5%8D%9A%E5%A3%AB%E5%AD%A6%E4%BD%8D%2E+%E4%B8%BB%E8%A6%81+%E7%A0%94%E7%A9%B6%E6%96%B9%E5%90%91%E4%B8%BA%E8%A7%86%E9%A2%91%E7%90%86%E8%A7%A3%2C+%E5%A4%9A%E6%A8%A1%E6%80%81%E5%A4%A7%E8%AF%AD%E8%A8%80+%E6%A8%A1%E5%9E%8B%2E+E%2Dmail%3A+jinglinglin%40pjlab%2Eorg%2Ecn+%28JING+Ling%2DLin+Young+res)