RL makes MLLMs see better than SFT

Junha Song, Sangdoo Yun, Dongyoon Han, Jaegul Choo, Byeongho Heo

22 Oct 2025 3 min read

AI-generated image, based on the article abstract

Quick Insight

How Reinforcement Learning Helps AI See Better Than Traditional Training

Ever wondered why some AI can describe a photo with uncanny detail while others miss the obvious? Scientists discovered that a new training trick called reinforcement learning (RL) makes multimodal AI models “see” images far sharper than the older supervised finetuning (SFT) method. Think of it like teaching a child to recognize a dog by rewarding every correct guess, rather than just showing a textbook of dog pictures. This reward‑based learning sharpens the AI’s visual brain, letting it focus on the right parts of a picture—like spotting a tiny bird on a distant branch. The result? AI that answers visual questions more accurately, even with far less training time. The researchers turned this insight into a simple recipe named PIVOT, which builds stronger “eyes” for AI without the massive computing costs of traditional methods. Imagine your phone instantly understanding a scene with the precision of a seasoned photographer. This breakthrough shows that smarter training, not just bigger models, can bring us closer to truly perceptive machines. The future of AI vision just got a lot clearer.

Short Review

Advancing Multimodal Language Models: A Deep Dive into Vision Encoder Optimization

This insightful study addresses a critical gap in Multimodal Language Model (MLLM) research by investigating how post-training strategies fundamentally reshape the performance of their vision encoders. Challenging the assumption that MLLM capabilities primarily stem from the LLM backbone, the research meticulously compares Supervised Finetuning (SFT) with Reinforcement Learning (RL), specifically Direct Preference Optimization (DPO). Through diverse experiments, including Visual Question Answering (VQA) benchmarks, ImageNet classification, and gradient visualization, the authors demonstrate that RL-based training yields superior, more precisely localized visual representations. The work culminates in the introduction of PIVOT (Preference-Instructed Vision OpTimization), an efficient method that significantly enhances vision encoders, even outperforming larger, more computationally intensive models.

Critical Evaluation

Strengths

The study's primary strength lies in its novel focus on the often-overlooked vision encoder within MLLMs, providing much-needed empirical evidence for its critical role. The rigorous methodology, employing controlled comparisons between DPO and SFT across various tasks and scales, robustly demonstrates DPO's advantages in improving object localization and vision-language alignment. Furthermore, the introduction of the PIVOT method offers a practical, computationally efficient solution for building stronger vision backbones, representing a significant step forward for MLLM development.

Weaknesses

While the findings are compelling, the study could benefit from further exploration into the mechanistic understanding of how DPO precisely reshapes visual representations at a deeper architectural level. Additionally, while demonstrating clear advantages, the generalizability of PIVOT across an even broader spectrum of MLLM architectures and diverse real-world applications, beyond the evaluated benchmarks, warrants continued investigation to fully ascertain its universal applicability and long-term stability.

Implications

This research carries profound implications for the future of MLLM development, shifting the paradigm towards optimizing vision components rather than solely focusing on language models. By demonstrating the power of preference-based learning for vision encoders, it paves the way for more capable, efficient, and robust MLLMs, particularly in tasks requiring fine-grained visual understanding. The computational efficiency offered by PIVOT also suggests a more sustainable path for advancing these complex models, making high-performance MLLMs more accessible.

Conclusion

This article makes a substantial contribution to the field of multimodal AI, offering both a foundational understanding of how training strategies impact MLLM vision and a practical, innovative solution. By highlighting the critical role of the vision encoder and introducing PIVOT, the authors provide an effective and efficient recipe for building next-generation MLLMs. This work is poised to inspire further research into vision-centric optimization, ultimately leading to more powerful and resource-efficient AI systems.