LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs
Authors: name: Omkar Thawakar, name: Dinura Dissanayake, name: Ketan More, name: Ritesh Thawkar, name: Ahmed Heakl, name: Noor Ahsan, name: Yuhao Li, name: Mohammed Zumri, name: Jean Lahoud, name: Rao Muhammad Anwer, name: Hisham Cholakkal, name: Ivan Laptev, name: Mubarak Shah, name: Fahad Shahbaz Khan, name: Salman Khan
Organization: Mohamed Bin Zayed University of Artificial Intelligence

Reasoning is a fundamental capability for solving complex multi-step
problems, particularly in visual contexts where sequential step-wise
understanding is essential. Existing approaches lack a comprehensive framework
for evaluating visual reasoning and do not emphasize step-wise problem-solving.
To this end, we propose a comprehensive framework for advancing step-by-step
visual reasoning in large language models (LMMs) through three key
contributions. First, we introduce a visual reasoning benchmark specifically
designed to evaluate multi-step reasoning tasks. The benchmark presents a
diverse set of challenges with eight different categories ranging from complex
visual perception to scientific reasoning with over 4k reasoning steps in
total, enabling robust evaluation of LLMs' abilities to perform accurate and
interpretable visual reasoning across multiple steps. Second, we propose a
novel metric that assesses visual reasoning quality at the granularity of
individual steps, emphasizing both correctness and logical coherence. The
proposed metric offers deeper insights into reasoning performance compared to
traditional end-task accuracy metrics. Third, we present a new multimodal
visual reasoning model, named LlamaV-o1, trained using a multi-step curriculum
learning approach, where tasks are progressively organized to facilitate
incremental skill acquisition and problem-solving. The proposed LlamaV-o1 is
designed for multi-step reasoning and learns step-by-step through a structured
training paradigm. Extensive experiments show that our LlamaV-o1 outperforms
existing open-source models and performs favorably against close-source
proprietary models. Compared to the recent Llava-CoT, our LlamaV-o1 achieves an
average score of 67.3 with an absolute gain of 3.8\% across six benchmarks
while being 5 times faster during inference scaling. Our benchmark, model, and
code are publicly available.