Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models

The integration of reinforcement learning (RL) into the reasoning capabilities of Multimodal Large Language Models (MLLMs) has rapidly emerged as a transformative research direction. While MLLMs significantly extend Large Language Models (LLMs) to handle diverse modalities such as vision, audio, and video, enabling robust reasoning across multimodal inputs remains a major challenge. This survey systematically reviews recent advances in RL-based reasoning for MLLMs, covering key algorithmic designs, reward mechanism innovations, and practical applications. We highlight two main RL paradigms--value-free and value-based methods--and analyze how RL enhances reasoning abilities by optimizing reasoning trajectories and aligning multimodal information. Furthermore, we provide an extensive overview of benchmark datasets, evaluation protocols, and existing limitations, and propose future research directions to address current bottlenecks such as sparse rewards, inefficient cross-modal reasoning, and real-world deployment constraints. Our goal is to offer a comprehensive and structured guide to researchers interested in advancing RL-based reasoning in the multimodal era.

DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers

Scaling large models with long sequences across applications like language generation, video generation and multimodal tasks requires efficient sequence parallelism. However, existing sequence parallelism methods all assume a single sequence dimension and fail to adapt to multi-dimensional transformer architectures that perform attention calculations across different dimensions. This paper introduces Dynamic Sequence Parallelism (DSP), a novel approach to enable efficient sequence parallelism for multi-dimensional transformer models. The key idea is to dynamically switch the parallelism dimension according to the current computation stage, leveraging the potential characteristics of multi-dimensional attention. This dynamic dimension switching allows sequence parallelism with minimal communication overhead compared to applying traditional single-dimension parallelism to multi-dimensional models. Experiments show DSP improves end-to-end throughput by 42.0% to 216.8% over prior sequence parallelism methods.