GRIP-VLM: RL for Efficient Vision-Language Models

GRIP-VLM employs Reinforcement Learning for discrete Vision-Language Model pruning, achieving superior efficiency and adaptability.

May 14 at 8:01 PM6 min read

Diagram illustrating the GRIP-VLM framework for efficient Vision-Language Model pruning. — The GRIP-VLM framework utilizes Reinforcement Learning for adaptive token pruning in Vision-Language Models.

Visual TL;DR. VLM computational demands leads to Existing pruning limitations. Existing pruning limitations solves GRIP-VLM framework. GRIP-VLM framework uses RL for discrete optimization. RL for discrete optimization employs GRPO paradigm. GRPO paradigm enables Direct discrete search. RL for discrete optimization enables Direct discrete search. Direct discrete search leads to Superior efficiency.

VLM computational demands: escalating computational demands of VLMs driven by massive visual token processing
Existing pruning limitations: existing training-aware pruning falters under aggressive compression due to approximations
GRIP-VLM framework: novel framework for discrete vision-language model pruning
RL for discrete optimization: formulates visual token pruning as a Markov Decision Process
GRPO paradigm: Group Relative Policy Optimization augmented by supervised warm-up
Direct discrete search: directly navigates the discrete search space for effective pruning decisions
Superior efficiency: achieves unprecedented efficiency and adaptability in VLMs

Visual TL;DRQuickExplainDeeper

The escalating computational demands of Vision-Language Models (VLMs), driven by massive visual token processing, present a critical bottleneck for scalability. Existing training-aware pruning techniques often falter under aggressive compression due to their reliance on continuous approximations for an inherently discrete problem.

Unlocking Discrete Optimization with Reinforcement Learning

To circumvent the limitations of gradient-based methods that frequently trap optimization in local minima, the GRIP-VLM framework introduces a novel approach. By formulating visual token pruning as a Markov Decision Process, GRIP-VLM leverages a Group Relative Policy Optimization (GRPO) paradigm. This RL-driven strategy, augmented by supervised warm-up, directly navigates the discrete search space, enabling more effective and less constrained pruning decisions. This marks a significant departure from prior attempts at Vision-Language Model pruning.

Adaptive Pruning for Unprecedented Efficiency

GRIP-VLM's architecture features a lightweight agent equipped with a budget-aware scorer. This agent dynamically assesses the importance of each token and can adapt to any compression ratio without requiring a full retraining cycle. Extensive evaluations across diverse multimodal benchmarks confirm GRIP-VLM's superiority over heuristic and supervised baselines. The framework consistently achieves a more favorable Pareto frontier, delivering up to a 15% inference speedup while maintaining accuracy, thereby addressing a core challenge in Vision-Language Model pruning.

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#AI Research #Vision-Language Models #Model Pruning #Reinforcement Learning #Deep Learning Efficiency