The recent developments in the field of multimodal reasoning and large language models (LLMs) have seen a significant shift towards enhancing structured and systematic reasoning capabilities. Researchers are increasingly focusing on integrating 'slow thinking' frameworks that allow models to engage in step-by-step reasoning processes, which are crucial for tasks requiring deep understanding and logical coherence. This approach not only improves the precision and reliability of model outputs but also enables better generalization across diverse domains, including those with open-ended solutions. The incorporation of process supervision and nonlinear reward shaping in policy optimization has further advanced the field, providing more robust methods for training models to avoid logical errors and redundant reasoning. Notably, these advancements are being driven by the creation of specialized datasets and the development of novel inference techniques that leverage multistage reasoning and atomic step fine-tuning. These innovations collectively push the boundaries of what LLMs can achieve in complex, reasoning-intensive tasks, setting new benchmarks in performance and applicability.

Noteworthy Papers:

• LLaVA-o1 introduces a structured multistage reasoning approach that significantly outperforms larger models on multimodal reasoning benchmarks.
• PSPO* proposes a nonlinear reward shaping method for process supervision, demonstrating consistent improvements in mathematical reasoning tasks.
• AtomThink integrates 'slow thinking' into multimodal LLMs, achieving substantial accuracy gains in mathematical reasoning by focusing on atomic step-by-step reasoning.