GLM-4.1V-Thinking and GLM-4.5V: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

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Identified Weaknesses

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Paper Summary

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GLM-4.1V and GLM-4.5V: New Multimodal Models for Enhanced Visual and Language Understanding

The paper introduces two vision-language models, GLM-4.1V and GLM-4.5V, trained using a novel framework focused on scalable reinforcement learning. They achieve state-of-the-art performance on numerous benchmarks, especially in STEM problem-solving, but real-world applications and comparisons with closed-source models need further investigation.

Possible Conflicts of Interest

The authors are affiliated with Zhipu AI & Tsinghua University, indicating potential conflicts of interest related to funding or research bias.

Identified Weaknesses

Limited Comparison with Closed-Source Models

The paper presents a novel approach but doesn't delve deeply into comparisons with commercial counterparts, hindering a full grasp of its real-world impact.

Predominantly Benchmark-Based Evaluation

The evaluation focuses primarily on academic benchmarks, lacking real-world application testing to fully assess practical performance.

Scope for Enhanced Scenario Diversity

While multi-modal tasks are covered, the paper could benefit from exploring more interactive and dynamic scenarios.

Rating Explanation

The research presents a substantial advancement in multimodal reasoning, introducing novel models with impressive benchmark results. However, limitations in comparison scope and real-world application testing warrant a rating of 4.

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