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AlphaMaze: Enhancing Large Language Models' Spatial Intelligence via GRPO
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AlphaMaze: Enhancing Large Language Models' Spatial Intelligence via GRPO
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Large Language Models (LLMs) have demonstrated impressive capabilities in language processing, yet they often struggle with tasks requiring genuine visual spatial reasoning. In this paper, we introduce a novel two-stage training framework designed to equip standard LLMs with visual reasoning abilities for maze navigation. First, we leverage Supervised Fine Tuning (SFT) on a curated dataset of tokenized maze representations to teach the model to predict step-by-step movement commands. Next, we apply Group Relative Policy Optimization (GRPO)-a technique used in DeepSeekR1-with a carefully crafted reward function to refine the model's sequential decision-making and encourage emergent chain-of-thought behaviors. Experimental results on synthetically generated mazes show that while a baseline model fails to navigate the maze, the SFT-trained model achieves 86% accuracy, and further GRPO fine-tuning boosts accuracy to 93%. Qualitative analyses reveal that GRPO fosters more robust and self-corrective reasoning, highlighting the potential of our approach to bridge the gap between language models and visual spatial tasks. These findings offer promising implications for applications in robotics, autonomous navigation, and other domains that require integrated visual and sequential reasoning.
Forward citations
Cited by 6 Pith papers
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A new video benchmark and post-training recipe claim to improve VLMs' counterfactual 'what if' reasoning, but the reported gains likely come from training on the test set.
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A new maze-navigation benchmark for LLMs reports that reasoning models outperform standard ones, but the link from performance gaps to a lack of persistent self-awareness is an overreach.
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