# Aqal: The First Urdu Reasoning-Optimized Large Language Model

> Aqal is the world's first reasoning-optimized large language model specifically tailored for Urdu. Through a three-stage training process (continuous pre-training, supervised fine-tuning, and GRPO reinforcement learning), it significantly enhances multi-step reasoning, logical consistency, and the correctness of final answers.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-06T05:37:12.000Z
- 最近活动: 2026-05-06T05:51:56.828Z
- 热度: 141.8
- 关键词: 乌尔都语, 大语言模型, 推理模型, GRPO, 持续预训练, 监督微调, 低资源语言, 多语言AI
- 页面链接: https://www.zingnex.cn/en/forum/thread/aqal-3e62a91d
- Canonical: https://www.zingnex.cn/forum/thread/aqal-3e62a91d
- Markdown 来源: floors_fallback

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## Aqal: Introduction to the World's First Urdu Reasoning-Optimized Large Language Model

Aqal is the world's first reasoning-optimized large language model specifically designed for Urdu. Through a three-stage training process (continuous pre-training, supervised fine-tuning, and GRPO reinforcement learning), it significantly improves multi-step reasoning, logical consistency, and the correctness of final answers, filling the gap in high-quality reasoning models for low-resource languages like Urdu.

## Background: AI Reasoning Challenges for Urdu as a Low-Resource Language

Urdu is one of the official languages of Pakistan, with over 170 million speakers worldwide. However, as a low-resource language, traditional large language models face issues such as scarce training data, insufficient reasoning capabilities, and poor logical consistency. The Aqal project aims to fill this gap and explore systematic methods to enhance the reasoning performance of Urdu models.

## Three-Stage Training Process: Building Urdu Reasoning Capabilities

Aqal uses a three-stage training approach:
1. **Continuous Pre-training**: Builds foundational language capabilities based on multi-domain Urdu corpora;
2. **Supervised Fine-tuning**: Introduces high-quality reasoning datasets to train the model to follow instructions and perform multi-step reasoning;
3. **GRPO Reinforcement Learning**: Enhances reasoning quality through policy optimization and reward modeling, addressing the instability issues of traditional reinforcement learning.

## Technical Architecture: Modular Design for Efficient Development and Evaluation

Aqal adopts a modular architecture:
- Environment Management: Conda-isolated environment;
- Dependencies: Python 3.10+ and a clear requirements.txt;
- Code Structure: Includes main entry scripts, training modules (e.g., GRPO trainer), and evaluation tools (e.g., reasoning evaluation scripts), facilitating research and development.

## Significance and Outlook: Promoting AI Inclusivity and Low-Resource Language Development

The significance of Aqal includes:
- For the Urdu community: Provides support for mother-tongue education, business applications, and cultural heritage preservation;
- Technical Paradigm: The three-stage training process can serve as a reference for the development of models for other low-resource languages, promoting balanced global language technology development.

## Conclusion: Aqal Paves a New Direction for AI Reasoning in Low-Resource Languages

Aqal, through its systematic training method, builds high-quality reasoning models for low-resource languages and promotes AI democratization. We look forward to more breakthroughs in the Urdu AI field and encourage researchers to focus on the inclusivity and diversity of language technologies.