STELAR-VISION: Self-Topology-Aware Efficient Learning for Aligned Reasoning in Vision

TL;DR

STELAR-Vision introduces a topology-aware training framework for vision-language models, enhancing reasoning capabilities and output efficiency by incorporating diverse topological structures and reducing verbosity.

Contribution

The paper presents STELAR-Vision, a novel training framework that integrates topological reasoning structures and frugal output techniques into vision-language models.

Findings

Improves accuracy by 9.7% on key benchmarks.

Surpasses larger models in accuracy by 7.3%.

Outperforms existing methods on multiple OOD benchmarks.

Abstract

Vision-language models (VLMs) have made significant strides in reasoning, yet they often struggle with complex multimodal tasks and tend to generate overly verbose outputs. A key limitation is their reliance on chain-of-thought (CoT) reasoning, despite many tasks benefiting from alternative topologies like trees or graphs. To address this, we introduce STELAR-Vision, a training framework for topology-aware reasoning. At its core is TopoAug, a synthetic data pipeline that enriches training with diverse topological structures. Using supervised fine-tuning and reinforcement learning, we post-train Qwen2VL models with both accuracy and efficiency in mind. Additionally, we propose Frugal Learning, which reduces output length with minimal accuracy loss. On MATH-V and VLM-S2H, STELAR-Vision improves accuracy by 9.7% over its base model and surpasses the larger Qwen2VL-72B-Instruct by 7.3%. On five out-of-distribution benchmarks, it outperforms Phi-4-Multimodal-Instruct by up to 28.4% and LLaMA-3.2-11B-Vision-Instruct by up to 13.2%, demonstrating strong generalization. Compared to Chain-Only training, our approach achieves 4.3% higher overall accuracy on in-distribution datasets and consistently outperforms across all OOD benchmarks.