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arXiv · 2606.07602

Sample-Efficient Post-Training for LEGO Spatial-Physics Reasoning

Yuhuan Yuan·Zhouliang Yu·Minghao Liu·Weiyang Liu·Ge Lin Kan
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Abstract

LLM-based LEGO assembly requires both semantic grounding and physical feasibility. In this paper, we identify a data-induced failure mode, physhack, in which generated assemblies satisfy physical-validity constraints while remaining geometrically misaligned, semantically inconsistent, or poorly calibrated. To address this challenge, we propose a model-based data selection approach that uses only 5% of the original training data while improving semantic and structural alignment across multiple independent evaluators. We further introduce PVPO, a reinforcement learning method that couples physical-validity and voxel-space geometric rewards to strengthen the LEGO synthesis capabilities of LLMs. PVPO-trained DeepSeek-Llama-8B outperforms the frontier VLM Kimi-K3 across semantic, structural, and physical evaluation dimensions, even when Kimi-K3 is provided with a ground-truth image as an additional reference.

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Yuhuan Yuan, Zhouliang Yu, Minghao Liu, Weiyang Liu, Ge Lin Kan. 2026-05-29. Sample-Efficient Post-Training for LEGO Spatial-Physics Reasoning. https://arxiv.org/abs/2606.07602

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