The field of multimodal reasoning and foundation models is rapidly advancing, with a focus on developing more efficient, scalable, and generalizable models. Recent developments have centered around improving the reasoning capabilities of large language models (LLMs) and multimodal large language models (MLLMs) through novel training paradigms, architectures, and fine-tuning strategies. Notably, researchers are exploring the use of reinforcement learning, self-supervised learning, and multimodal fusion techniques to enhance model performance on complex tasks such as math problem solving, visual question answering, and medical diagnosis. Furthermore, there is a growing interest in developing more interpretable and transparent models, with techniques such as confidence calibration and step-by-step reasoning traces being investigated. Overall, the field is moving towards creating more powerful, flexible, and reliable models that can effectively reason and generalize across multiple domains and tasks. Noteworthy papers include JanusDNA, which introduces a novel bidirectional DNA foundation model, and Infi-MMR, which proposes a curriculum-based approach to unlocking multimodal reasoning in small language models. Additionally, BioReason presents a pioneering architecture that integrates a DNA foundation model with a large language model, enabling multimodal biological reasoning.
Advancements in Multimodal Reasoning and Foundation Models
Sources
JanusDNA: A Powerful Bi-directional Hybrid DNA Foundation Model
Scaling Up Biomedical Vision-Language Models: Fine-Tuning, Instruction Tuning, and Multi-Modal Learning
Decoupled Visual Interpretation and Linguistic Reasoning for Math Problem Solving
Bridging Supervised Learning and Reinforcement Learning in Math Reasoning
Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge
ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation
Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start
Unsupervised Post-Training for Multi-Modal LLM Reasoning via GRPO
Decomposing Elements of Problem Solving: What "Math" Does RL Teach?
Infi-MMR: Curriculum-based Unlocking Multimodal Reasoning via Phased Reinforcement Learning in Multimodal Small Language Models
Elicit and Enhance: Advancing Multimodal Reasoning in Medical Scenarios
MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibration
Uni-MuMER: Unified Multi-Task Fine-Tuning of Vision-Language Model for Handwritten Mathematical Expression Recognition
BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model
Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation
Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better