Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning (2411.18203v5)

Abstract: Vision-LLMs (VLMs) have shown remarkable advancements in multimodal reasoning tasks. However, they still often generate inaccurate or irrelevant responses due to issues like hallucinated image understandings or unrefined reasoning paths. To address these challenges, we introduce Critic-V, a novel framework inspired by the Actor-Critic paradigm to boost the reasoning capability of VLMs. This framework decouples the reasoning process and critic process by integrating two independent components: the Reasoner, which generates reasoning paths based on visual and textual inputs, and the Critic, which provides constructive critique to refine these paths. In this approach, the Reasoner generates reasoning responses according to text prompts, which can evolve iteratively as a policy based on feedback from the Critic. This interaction process was theoretically driven by a reinforcement learning framework where the Critic offers natural language critiques instead of scalar rewards, enabling more nuanced feedback to boost the Reasoner's capability on complex reasoning tasks. The Critic model is trained using Direct Preference Optimization (DPO), leveraging a preference dataset of critiques ranked by Rule-based Reward~(RBR) to enhance its critic capabilities. Evaluation results show that the Critic-V framework significantly outperforms existing methods, including GPT-4V, on 5 out of 8 benchmarks, especially regarding reasoning accuracy and efficiency. Combining a dynamic text-based policy for the Reasoner and constructive feedback from the preference-optimized Critic enables a more reliable and context-sensitive multimodal reasoning process. Our approach provides a promising solution to enhance the reliability of VLMs, improving their performance in real-world reasoning-heavy multimodal applications such as autonomous driving and embodied intelligence.

• The paper introduces Critic-V, a novel Reasoner-Critic architecture that refines multimodal reasoning with dynamic natural language feedback.
• It leverages Direct Preference Optimization to use nuanced critiques, boosting performance by up to 11.8% on key benchmarks.
• The framework enhances VLM accuracy in high-stakes applications like autonomous driving by ensuring more reliable and context-aware inference.

Critic-V: Enhancing Multimodal Reasoning in Vision-LLMs Through Critique

The need for robust reasoning in Vision-LLMs (VLMs) underscores the limitations that current models face in generating accurate responses to multimodal inputs. Despite advancements in VLMs like GPT-4V and others, challenges persist due to hallucinated image understandings or unsophisticated reasoning paths, affecting their utility in complex applications such as autonomous driving. The paper “Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning” addresses these issues by introducing Critic-V, a framework inspired by the Actor-Critic paradigm used in reinforcement learning.

Framework Overview

Critic-V employs a Reasoner-Critic architecture wherein VLMs are decoupled into two modules. The Reasoner generates reasoning paths based on visual and textual inputs, while the Critic module provides critiques to refine these paths. Unlike typical reinforcement learning frameworks that use scalar rewards, this approach leverages natural language critiques. The Critic uses Direct Preference Optimization (DPO) trained on a data set with preferences ranked by Rule-based Reward (RBR). This method allows the Critic to offer more nuanced, context-sensitive feedback, which iteratively improves the Reasoner's performance.

Evaluation and Results

The paper underscores significant improvements achieved using the Critic-V framework across multiple benchmarks. For instance, Critic-V outperforms current methods on 5 out of 8 evaluation benchmarks, exemplified by improvements in reasoning-heavy tasks like MathVista (enhancing performance by 11.8%). The paper presents experimental results showing that incorporating Critic-V improves model accuracy and efficiency, especially on tasks where precise inference is critical.

Implications and Future Directions

The integration of a critique-based feedback mechanism addresses a key limitation of existing VLMs. By shifting from internal model-centric reasoning improvements to an external feedback structure, Critic-V provides a pathway toward more reliable and contextually aware multimodal applications. This is particularly relevant for applications demanding high reliability in real-world scenarios, such as autonomous technologies and embodied AI systems.

Theoretical implications suggest a promising shift in how reinforcement learning can be applied to VLMs, with critiques replacing traditional scalar rewards, allowing for richer interaction and more precise feedback loops. Practically, this approach could lead to safer and more effective autonomous systems capable of navigating complex, dynamic environments.

Future research may focus on expanding the Critic-V approach to various modalities and task types, potentially integrating more varied forms of feedback and critique mechanisms. Additionally, exploring the scalability of Critic-V in extensive deployment scenarios and understanding its limitations in dynamic real-world settings would be beneficial.

In summary, Critic-V presents a compelling methodological advancement in addressing the nuances of multimodal reasoning in VLMs. By integrating dynamic critique mechanisms, this framework elevates the potential for developing more adaptable and reliable AI systems across diverse applications.