Extending Falcon-H1R's hybrid-parallel architecture to Vision-Language Models (VLMs) will enable 'Visual Test-Time Scaling,' where the model iteratively re-attends to specific image regions based on intermediate reasoning gaps to solve multi-step visual logic puzzles.

Motivation

While Falcon-H1R demonstrates efficient scaling for text, visual reasoning (e.g., interpreting complex charts or geometry) suffers from single-pass encoding bottlenecks. Applying the paper's RL scaling and hybrid verification to the visual domain could allow a model to 'look closer' or 'double-check' visual features dynamically.

Proposed Method

Integrate a Vision Transformer (ViT) encoder with Falcon-H1R. Modify the RL training pipeline (PPO) using visual reasoning datasets (e.g., MathVista). Define a 'glimpse' action where the model can request re-encoded visual tokens from specific image patches if the DeepConf score is low during the reasoning chain generation.

Expected Contribution

The first demonstration of 'Test-Time Visual Scaling' in SLMs, proving that iterative computation can compensate for smaller visual encoders in complex multimodal tasks.

Required Resources

Multimodal datasets (MathVista/ChartQA), significant GPU resources for RL training on VLMs, and expertise in multimodal architecture design.

Source Paper

Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling

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