The field of artificial intelligence is witnessing significant advancements in multimodal reasoning, with a focus on enhancing the capabilities of large language models and vision-language models. Recent developments have explored innovative approaches to improve mathematical reasoning, visual reasoning, and spatial reasoning. These advancements have led to improved performance on various benchmarks and have the potential to democratize access to high-performance AI research. Notably, researchers have demonstrated the ability to train solid mathematical reasoning models using limited computational resources, and have proposed novel training-free approaches to enhance reasoning in large vision-language models. The use of Monte Carlo Tree Search and Self-Reward mechanisms has shown promise in improving multimodal mathematical reasoning, while the incorporation of drawing operations has enabled large vision-language models to reason through elementary visual manipulation. Some noteworthy papers include:

• VReST, which proposes a novel training-free approach that enhances reasoning in large vision-language models through Monte Carlo Tree Search and Self-Reward mechanisms.
• Athena: Enhancing Multimodal Reasoning with Data-efficient Process Reward Models, which presents a multimodal process reward model that evaluates the reward score for each step in solving complex reasoning problems.
• Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing, which proposes a novel paradigm that enables large vision-language models to reason through elementary drawing operations in the visual space.