The Rise of Open Source Reasoning Models: Welcome Qwen QwQ and QvQ

Open-source reasoning models like Qwen QwQ and QvQ are redefining the landscape of large reasoning models (LRMs). These models excel in transparency, logical consistency, and domain-specific fine-tuning, setting new benchmarks for AI reasoning. This article explores their technological advancements, their impact on reasoning AI, and the implications for developers and organizations embracing this next-generation AI paradigm.

From Generation to Reasoning—The Shift in AI Paradigms

The evolution from generative capabilities to reasoning-first approaches marks a transformative shift in AI. While traditional LLMs have excelled at generating fluent text, reasoning models now focus on step-by-step transparency and logic, enabling more accurate and interpretable outputs.

The Limitations of Traditional LLMs

Large Language Models (LLMs) such as GPT-4 and Claude have dominated AI innovation, excelling at generating fluent and contextually relevant text. However, their reliance on pattern recognition and autoregressive token prediction often leaves them struggling with tasks requiring structured reasoning or transparency in their thought processes​​.

In contrast, the emergence of Large Reasoning Models (LRMs) represents a paradigm shift. LRMs prioritize structured reasoning, breaking down problems into manageable steps through methods like Chain-of-Thought (CoT) reasoning. This focus on logical progression and interpretability allows these models to excel in areas where precision and clarity are critical, such as mathematics, coding, and scientific problem-solving​.

Why Open Source Matters in Reasoning

Open-source projects like OpenR and OpenRFT have played a pivotal role in advancing reasoning capabilities. These frameworks provide tools for integrating reinforcement learning techniques and process reward models, enabling developers to fine-tune models for domain-specific tasks while ensuring transparency​​.

Open-source reasoning models democratize access to state-of-the-art AI, empowering smaller organizations and independent developers to innovate without the cost barriers of proprietary systems. By fostering collaboration and community-driven development, these initiatives accelerate advancements in reasoning AI while ensuring alignment with diverse user needs​​.

Qwen QwQ and QvQ—A New set of Frontier multimodal Open Reasoning Mode

Open-source reasoning models like Qwen QwQ and QvQ exemplify the forefront of AI advancements. These models combine transparency, fine-tuning, and multimodal capabilities to set new benchmarks in structured reasoning and problem-solving.

What Sets Qwen QwQ Apart

Qwen QwQ represents a significant leap forward in reasoning AI. Designed for iterative problem-solving, it excels in breaking down complex, multi-step tasks across domains such as financial analysis and logic puzzles. By employing advanced fine-tuning strategies, Qwen QwQ achieves high accuracy in numeric reasoning, as demonstrated by its 90.6% score on MATH-500​​.

A standout feature of Qwen QwQ is its ability to optimize memory retention during multi-turn conversations, reducing redundancy and increasing consistency. This is particularly evident in its capacity for nuanced contextual reasoning, enabling more accurate interpretations of ambiguous queries. Additionally, its integration of recursive reasoning allows the model to backtrack and refine solutions, addressing a critical limitation of earlier models like GPT-o1​​.

However, challenges remain. Open-source versions of Qwen QwQ face safety concerns and occasional struggles with language mixing during reasoning tasks, underscoring the need for robust evaluation in real-world applications​​.

QvQ’s Vision-Reasoning Fusion

QvQ is a cutting-edge multimodal reasoning model that combines visual and textual inputs to tackle complex tasks requiring a deep understanding of context across modalities. Built on the Qwen2 architecture, QvQ leverages advanced visual reasoning capabilities, enabling it to excel in tasks such as image-to-text reasoning and multimodal problem-solving​​.

Its achievements on benchmarks like the Multimodal Massive Multi-task Understanding (MMMU) test, where it scored 70.3%, highlight its multidisciplinary potential. This performance stems from innovative architectural improvements, such as the incorporation of grouped query attention and dual chunk attention, which enhance its ability to process long-context and multimodal data​​.

Despite its strengths, QvQ is not without limitations. The model occasionally exhibits issues with maintaining focus during multi-step visual reasoning, leading to hallucinations. Furthermore, its capabilities in basic recognition tasks, such as identifying objects or entities in images, do not surpass those of its predecessor, Qwen2-VL​.

Core Technologies Behind Modern Reasoning Models

The success of modern reasoning models lies in their ability to emulate human-like thought processes through innovative techniques. Key advancements such as Chain-of-Thought (CoT) reasoning and Reinforcement Fine-Tuning (RFT) empower these models to tackle complex, multi-step tasks with remarkable accuracy and transparency.

Chain of Thought and Beyond

Chain-of-Thought (CoT) reasoning, a pivotal innovation, enables large reasoning models (LRMs) to solve problems step by step, mirroring human cognitive processes. This method improves not only reasoning accuracy but also interpretability by producing intermediate steps before arriving at the final answer. Techniques like Tree-of-Thought and Graph-of-Thought expand on CoT, allowing models to explore multiple pathways simultaneously for complex problem-solving​​.

For example, Tree-of-Thought organizes reasoning into a branching structure, systematically evaluating alternatives, while ReAct combines reasoning with real-world actions, creating a dynamic interplay between decision-making and execution. These advancements highlight the growing importance of structured reasoning in improving model reliability​.

Reinforcement Fine-Tuning (RFT) and Process Reward Models (PRMs)

Reinforcement Fine-Tuning (RFT) marks a shift in training paradigms, moving beyond supervised learning to incorporate dynamic feedback loops. By integrating Process Reward Models (PRMs), RFT evaluates reasoning steps at a granular level, providing dense feedback rather than focusing solely on end outcomes. This ensures models not only generate accurate results but also align with human-like reasoning​​.

PRMs have been instrumental in elevating reasoning performance. They assign rewards to intermediate steps, encouraging models to refine their logic iteratively. This step-by-step feedback is particularly effective in tasks requiring complex decision-making, such as mathematical reasoning or multi-turn dialogues. By combining PRMs with techniques like Monte Carlo Tree Search (MCTS), reasoning models achieve higher accuracy and robustness in diverse domains​​.

Comparative Benchmarks of Reasoning Models

The rapid evolution of reasoning models has led to significant advancements in performance across a variety of benchmarks. Models such as GPT-o1, Qwen QwQ, and QvQ have demonstrated exceptional capabilities in reasoning-intensive tasks, often outperforming traditional LLMs in domains requiring structured logic and transparency.

Quantitative Performance Across Benchmarks

Mathematical Reasoning
Qwen QwQ has achieved remarkable results on the MATH-500 benchmark, scoring 90.6%, which places it among the top-performing open-source reasoning models​. This surpasses the performance of GPT-o1, which, while strong in structured reasoning, has not been as optimized for iterative problem-solving. Similarly, QvQ demonstrates strong numerical reasoning capabilities, with significant improvements in visual-mathematical tasks, leveraging its multimodal design​​.

Coding and Logical Reasoning
On coding benchmarks such as HumanEval and MBPP, reasoning models have shown their ability to break down complex problems into manageable sub-steps. Qwen QwQ, optimized for domain-specific fine-tuning, has consistently outperformed generalist models. Its recursive reasoning capabilities ensure improved memory retention and reduced errors in multi-turn tasks​​.

Comparing Strengths and Weaknesses

• Qwen QwQ: Strong in numeric and logical reasoning, excelling in tasks requiring multi-step iterations. However, it struggles with issues like language mixing and consistency in open-source environments​​.
• QvQ: Excels in multimodal reasoning tasks but has limitations in retaining focus during long visual reasoning sequences. Its hallucination risks also highlight areas needing improvement​.
• GPT-o1: A generalist reasoning model that established the baseline for structured logic but lacks some of the advanced fine-tuning and iterative capabilities found in Qwen QwQ​​.

Implications of Benchmark Results

These benchmarks underscore the growing potential of reasoning models to tackle complex, high-stakes tasks. By leveraging reinforcement fine-tuning and multimodal integration, models like Qwen QwQ and QvQ pave the way for a new era of AI that blends transparency, accuracy, and adaptability across domains. However, challenges such as scalability, safety, and real-world validation remain pivotal to their success.

Industry Use Cases of Reasoning Models

Reasoning models like Qwen QwQ and QvQ are driving transformative applications across diverse industries. By combining advanced reasoning capabilities with multimodal inputs, these models address complex challenges that traditional LLMs struggle to solve. Below are key domains where reasoning models are creating significant impact.

1. Financial Analysis and Risk Assessment

Qwen QwQ’s ability to handle iterative problem-solving and logical reasoning makes it a powerful tool for financial analysis. Tasks such as fraud detection, credit risk assessment, and market trend forecasting benefit from its structured reasoning capabilities. For example:

• Fraud Detection: The model identifies anomalous patterns in transaction data, combining numeric and contextual reasoning to flag potential fraud​​.
• Portfolio Optimization: By analyzing market data and predicting trends, reasoning models help financial advisors make data-driven decisions.

2. Healthcare Diagnostics and Research

Multimodal reasoning models like QvQ are revolutionizing healthcare by integrating textual and visual data for diagnostics. Their ability to analyze patient records, combine them with medical imaging, and reason through potential outcomes is paving the way for:

• Medical Imaging Analysis: QvQ identifies abnormalities in X-rays or MRIs while cross-referencing patient histories to suggest diagnoses​.
• Drug Discovery: By leveraging large datasets, reasoning models can propose hypotheses and analyze molecular interactions, accelerating drug development​.

3. Education and Adaptive Learning

Reasoning models are enhancing personalized learning experiences by dynamically adapting to students’ needs. For instance:

• Math Problem Solving: Qwen QwQ excels in guiding students through step-by-step solutions, helping them grasp complex mathematical concepts​​.
• Language Learning: By generating nuanced responses and correcting errors, these models simulate natural conversations to improve language skills.

4. Legal Analysis and Contract Review

Legal professionals leverage reasoning models for tasks like contract analysis and case law research. With their structured logic and ability to process vast amounts of text, reasoning models:

• Review Contracts for Risks: Highlight potential ambiguities or risks in contract clauses.
• Summarize Case Law: Extract relevant information from lengthy legal documents, saving time for lawyers​​.

5. Scientific Research and Engineering

Reasoning models like Qwen QwQ are accelerating progress in scientific research and engineering design by solving complex equations and analyzing experimental data:

• Physics Simulations: Simulate multi-step calculations in thermodynamics or quantum mechanics.
• Engineering Designs: Optimize designs using iterative reasoning to meet safety and efficiency standards​.

6. Retail and E-Commerce

Retailers are adopting reasoning models to enhance customer experiences and operational efficiency:

• Personalized Recommendations: By combining multimodal inputs such as product images and customer reviews, QvQ delivers tailored suggestions​.
• Inventory Management: Predict demand fluctuations and optimize stock levels with advanced reasoning capabilities.

Open Challenges and the Future of Reasoning Models

The rise of reasoning models like Qwen QwQ and QvQ signals a new era for AI, where transparency, structured reasoning, and multimodal capabilities take center stage. However, realizing their full potential requires addressing key challenges that affect their scalability, safety, and accessibility.

Addressing Bias and Safety

Reasoning models face ongoing concerns around biases in training data and the ethical implications of their outputs. Issues such as hallucinations, recursive reasoning loops, and inconsistencies in open-source implementations highlight the need for rigorous evaluation and mitigation strategies. Enhancing transparency through methods like Chain-of-Thought reasoning and process-level supervision can help build user trust​​.

Developing robust safety protocols and integrating real-time feedback mechanisms are crucial for deploying these models in high-stakes environments such as healthcare, finance, and legal analysis. Without these safeguards, the potential misuse or unintended consequences of reasoning models could overshadow their benefits​.

Overcoming Scalability Barriers

The computational demands of reasoning models present a significant barrier to entry, particularly for smaller organizations or developers with limited resources. Advanced techniques such as model quantization, parameter-efficient fine-tuning, and distributed inference are promising avenues for making these models more accessible without compromising performance​​.

Efforts to adapt reasoning models for edge devices and resource-constrained environments will democratize their use, enabling applications in underserved regions and industries. This scalability will also be vital for fostering innovation across a wider spectrum of developers and organizations​.

Bridging the Gap Between Research and Real-World Applications

While reasoning models excel on benchmarks, their real-world deployment often reveals gaps in performance and practicality. Developing domain-specific fine-tuning methods and expanding multimodal capabilities will be critical for addressing these shortcomings. Additionally, fostering collaboration between academia, industry, and open-source communities can accelerate the adoption and refinement of these technologies​​.

Looking Ahead

The future of reasoning models lies in their ability to integrate seamlessly into complex workflows, empowering industries with tools that are not only accurate but also interpretable and adaptable. By addressing the challenges of bias, scalability, and application readiness, reasoning models have the potential to redefine the boundaries of what AI can achieve.

With continued innovation and a commitment to ethical practices, reasoning models like Qwen QwQ and QvQ will not only elevate the standards of AI but also pave the way for new possibilities across domains, transforming the way we approach problem-solving in the age of artificial intelligence.

References:

Qwen/QVQ-72B-Preview · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Democratizing Reasoning Ability: Tailored Learning from Large Language Model

Large language models (LLMs) exhibit impressive emergent abilities in natural language processing, but their democratization is hindered due to huge computation requirements and closed-source nature. Recent research on advancing open-source smaller LMs by distilling knowledge from black-box LLMs has obtained promising results in the instruction-following ability. However, the reasoning ability which is more challenging to foster, is relatively rarely explored. In this paper, we propose a tailored learning approach to distill such reasoning ability to smaller LMs to facilitate the democratization of the exclusive reasoning ability. In contrast to merely employing LLM as a data annotator, we exploit the potential of LLM as a reasoning teacher by building an interactive multi-round learning paradigm. This paradigm enables the student to expose its deficiencies to the black-box teacher who then can provide customized training data in return. Further, to exploit the reasoning potential of the smaller LM, we propose self-reflection learning to motivate the student to learn from self-made mistakes. The learning from self-reflection and LLM are all tailored to the student’s learning status, thanks to the seamless integration with the multi-round learning paradigm. Comprehensive experiments and analysis on mathematical and commonsense reasoning tasks demonstrate the effectiveness of our method. The code will be available at https://github.com/Raibows/Learn-to-Reason.

arXiv.orgZhaoyang Wang

OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning

OpenAI’s recent introduction of Reinforcement Fine-Tuning (RFT) showcases the potential of reasoning foundation model and offers a new paradigm for fine-tuning beyond simple pattern imitation. This technical report presents \emph{OpenRFT}, our attempt to fine-tune generalist reasoning models for domain-specific tasks under the same settings as RFT. OpenRFT addresses two key challenges of lacking reasoning step data and the limited quantity of training samples, by leveraging the domain-specific samples in three ways: question augmentation, synthesizing reasoning-process data, and few-shot ICL. The evaluation is conducted on SciKnowEval, where OpenRFT achieves notable performance gains with only $100$ domain-specific samples for each task. More experimental results will be updated continuously in later versions. Source codes, datasets, and models are disclosed at: https://github.com/ADaM-BJTU/OpenRFT

arXiv.orgYuxiang Zhang

Towards Large Reasoning Models: A Survey on Scaling LLM Reasoning Capabilities

Language has long been conceived as an essential tool for human reasoning. The breakthrough of Large Language Models (LLMs) has sparked significant research interest in leveraging these models to tackle complex reasoning tasks. Researchers have moved beyond simple autoregressive token generation by introducing the concept of “thought” -- a sequence of tokens representing intermediate steps in the reasoning process. This innovative paradigm enables LLMs’ to mimic complex human reasoning processes, such as tree search and reflective thinking. Recently, an emerging trend of learning to reason has applied reinforcement learning (RL) to train LLMs to master reasoning processes. This approach enables the automatic generation of high-quality reasoning trajectories through trial-and-error search algorithms, significantly expanding LLMs’ reasoning capacity by providing substantially more training data. Furthermore, recent studies demonstrate that encouraging LLMs to “think” with more tokens during test-time inference can further significantly boost reasoning accuracy. Therefore, the train-time and test-time scaling combined to show a new research frontier -- a path toward Large Reasoning Model. The introduction of OpenAI’s o1 series marks a significant milestone in this research direction. In this survey, we present a comprehensive review of recent progress in LLM reasoning. We begin by introducing the foundational background of LLMs and then explore the key technical components driving the development of large reasoning models, with a focus on automated data construction, learning-to-reason techniques, and test-time scaling. We also analyze popular open-source projects at building large reasoning models, and conclude with open challenges and future research directions.

arXiv.orgFengli Xu

OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models

In this technical report, we introduce OpenR, an open-source framework designed to integrate key components for enhancing the reasoning capabilities of large language models (LLMs). OpenR unifies data acquisition, reinforcement learning training (both online and offline), and non-autoregressive decoding into a cohesive software platform. Our goal is to establish an open-source platform and community to accelerate the development of LLM reasoning. Inspired by the success of OpenAI’s o1 model, which demonstrated improved reasoning abilities through step-by-step reasoning and reinforcement learning, OpenR integrates test-time compute, reinforcement learning, and process supervision to improve reasoning in LLMs. Our work is the first to provide an open-source framework that explores the core techniques of OpenAI’s o1 model with reinforcement learning, achieving advanced reasoning capabilities beyond traditional autoregressive methods. We demonstrate the efficacy of OpenR by evaluating it on the MATH dataset, utilising publicly available data and search methods. Our initial experiments confirm substantial gains, with relative improvements in reasoning and performance driven by test-time computation and reinforcement learning through process reward models. The OpenR framework, including code, models, and datasets, is accessible at https://openreasoner.github.io.

arXiv.orgJun Wang

Qwen2 Technical Report

This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models. We release a comprehensive suite of foundational and instruction-tuned language models, encompassing a parameter range from 0.5 to 72 billion, featuring dense models and a Mixture-of-Experts model. Qwen2 surpasses most prior open-weight models, including its predecessor Qwen1.5, and exhibits competitive performance relative to proprietary models across diverse benchmarks on language understanding, generation, multilingual proficiency, coding, mathematics, and reasoning. The flagship model, Qwen2-72B, showcases remarkable performance: 84.2 on MMLU, 37.9 on GPQA, 64.6 on HumanEval, 89.5 on GSM8K, and 82.4 on BBH as a base language model. The instruction-tuned variant, Qwen2-72B-Instruct, attains 9.1 on MT-Bench, 48.1 on Arena-Hard, and 35.7 on LiveCodeBench. Moreover, Qwen2 demonstrates robust multilingual capabilities, proficient in approximately 30 languages, spanning English, Chinese, Spanish, French, German, Arabic, Russian, Korean, Japanese, Thai, Vietnamese, and more, underscoring its versatility and global reach. To foster community innovation and accessibility, we have made the Qwen2 model weights openly available on Hugging Face and ModelScope, and the supplementary materials including example code on GitHub. These platforms also include resources for quantization, fine-tuning, and deployment, facilitating a wide range of applications and research endeavors.

arXiv.orgAn Yang