Enhancing Lightweight Vision Language Models through Group Competitive Learning for Socially Compliant Navigation

• URL: http://arxiv.org/abs/2603.11447v1
• Date: Thu, 12 Mar 2026 02:16:04 GMT
• Title: Enhancing Lightweight Vision Language Models through Group Competitive Learning for Socially Compliant Navigation
• Abstract summary: Social robot navigation requires a sophisticated integration of scene semantics and human social norms.<n>Lightweight Vision Language Models (VLMs) enable efficient inference but often exhibit weaker reasoning and decision-making performance.<n>We propose Group Competitive Learning (GCL), a strategy designed to amplify the capabilities of lightweight VLMs.
• Score: 29.741263131312547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social robot navigation requires a sophisticated integration of scene semantics and human social norms. Scaling up Vision Language Models (VLMs) generally improves reasoning and decision-making capabilities for socially compliant navigation. However, increased model size incurs substantial computational overhead, limiting suitability for real-time robotic deployment. Conversely, lightweight VLMs enable efficient inference but often exhibit weaker reasoning and decision-making performance in socially complex environments. Achieving both strong reasoning ability and efficiency remains an open challenge. To bridge this gap, we propose Group Competitive Learning (GCL), a strategy designed to amplify the capabilities of lightweight VLMs. Our strategy introduces the Group Competitive Objective (GCO) to harmonize global semantics with distributional regularization, alongside Asymmetric Group Optimization (AGO) to explore the upper limits of model performance. Empirical evaluations on social navigation benchmarks demonstrate that GCL significantly elevates VLM performance. Specifically, GCL enables the Qwen2.5-VL-3B learner model and guide Qwen3-VL-4B to achieve an F1 score of 0.968 and 0.914, representing 40\% and 12\% improvement over vanilla supervised fine-tuning (SFT). Notably, under vanilla SFT, the 3B model initially trails the 8B model (F1: 0.692 vs. 0.755). However, through the GCL, the 3B model outperforms (28\%) the 8B baseline model. These results suggest that GCL provides an effective solution for achieving both high accuracy and computational efficiency in real-world deployment.

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