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Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains

arXiv:2602.13235v1 Announce Type: new Abstract: Visual Retrieval-Augmented Generation (VRAG) enhances Vision-Language Models (VLMs) by incorporating external visual documents to address a given query. Existing VRAG frameworks usually depend on rigid, pre-defined external tools to extend the perceptual capabilities of VLMs, typically by explicitly separating visual perception from subsequent reasoning processes. However, this decoupled design can lead to unnecessary loss of visual information, particularly when image-based operations such as cropping are applied. In this paper, we propose Lang2Act, which enables fine-grained visual perception and reasoning through self-emergent linguistic toolchains. Rather than invoking fixed external engines, Lang2Act collects self-emergent actions as linguistic tools and leverages them to enhance the visual perception capabilities of VLMs. To support this mechanism, we design a two-stage Reinforcement Learning (RL)-based training framework. Specific

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Yuqi Xiong, Chunyi Peng, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Yukun Yan, Shuo Wang, Yu Gu, Ge Yu · · 1 min read · 2 views
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arXiv:2602.13235v1 Announce Type: new Abstract: Visual Retrieval-Augmented Generation (VRAG) enhances Vision-Language Models (VLMs) by incorporating external visual documents to address a given query. Existing VRAG frameworks usually depend on rigid, pre-defined external tools to extend the perceptual capabilities of VLMs, typically by explicitly separating visual perception from subsequent reasoning processes. However, this decoupled design can lead to unnecessary loss of visual information, particularly when image-based operations such as cropping are applied. In this paper, we propose Lang2Act, which enables fine-grained visual perception and reasoning through self-emergent linguistic toolchains. Rather than invoking fixed external engines, Lang2Act collects self-emergent actions as linguistic tools and leverages them to enhance the visual perception capabilities of VLMs. To support this mechanism, we design a two-stage Reinforcement Learning (RL)-based training framework. Specifically, the first stage optimizes VLMs to self-explore high-quality actions for constructing a reusable linguistic toolbox, and the second stage further optimizes VLMs to exploit these linguistic tools for downstream reasoning effectively. Experimental results demonstrate the effectiveness of Lang2Act in substantially enhancing the visual perception capabilities of VLMs, achieving performance improvements of over 4%. All code and data are available at https://github.com/NEUIR/Lang2Act.

Executive Summary

The article 'Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains' introduces a novel approach to enhance Vision-Language Models (VLMs) by integrating a self-emergent linguistic toolchain mechanism. Unlike traditional Visual Retrieval-Augmented Generation (VRAG) frameworks that rely on rigid, pre-defined external tools, Lang2Act leverages self-emergent actions as linguistic tools to improve visual perception and reasoning. The proposed method employs a two-stage Reinforcement Learning (RL)-based training framework to optimize VLMs for self-exploration of high-quality actions and subsequent exploitation of these linguistic tools for downstream reasoning tasks. Experimental results show significant performance improvements, demonstrating the effectiveness of Lang2Act in enhancing the capabilities of VLMs.

Key Points

  • ▸ Introduction of Lang2Act for fine-grained visual reasoning through self-emergent linguistic toolchains.
  • ▸ Use of a two-stage RL-based training framework to optimize VLMs.
  • ▸ Achievement of over 4% performance improvement in visual perception capabilities.
  • ▸ Availability of code and data for further research and replication.

Merits

Innovative Approach

Lang2Act introduces a novel method for enhancing VLMs by leveraging self-emergent linguistic toolchains, which is a significant departure from traditional VRAG frameworks.

Performance Improvement

The method demonstrates substantial performance improvements in visual perception capabilities, making it a promising advancement in the field of VLMs.

Open-Source Resources

The availability of code and data supports reproducibility and further research, fostering a collaborative environment for academic and industrial applications.

Demerits

Complexity

The two-stage RL-based training framework may introduce complexity in implementation and require significant computational resources.

Generalization

The effectiveness of Lang2Act across diverse visual reasoning tasks and datasets needs further validation to ensure its generalizability.

Potential Overhead

The self-emergent linguistic toolchain mechanism might introduce additional overhead in terms of training time and computational resources.

Expert Commentary

The article presents a significant advancement in the field of Vision-Language Models by introducing Lang2Act, a method that leverages self-emergent linguistic toolchains to enhance visual perception and reasoning. The innovative approach of using a two-stage RL-based training framework is particularly noteworthy, as it allows for the self-exploration and exploitation of high-quality actions, leading to substantial performance improvements. The availability of code and data further supports the reproducibility and scalability of the method, making it a valuable contribution to the academic and industrial communities. However, the complexity of the training framework and the potential overhead in terms of computational resources are areas that warrant further investigation. Additionally, the generalizability of Lang2Act across diverse visual reasoning tasks and datasets needs to be thoroughly validated to ensure its robustness and effectiveness in real-world applications. Overall, Lang2Act represents a promising direction for the future of VLMs, with potential implications for both practical applications and policy considerations in the broader field of artificial intelligence.

Recommendations

  • ✓ Further validation of Lang2Act across diverse visual reasoning tasks and datasets to ensure generalizability.
  • ✓ Exploration of methods to reduce the computational overhead and complexity of the two-stage RL-based training framework.

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