Self-Supervised Learning Framework Improves Spatial Reasoning in Large Language Models Without Labeled Data
Researchers propose a self-supervised reinforcement learning framework that improves spatial reasoning in large reasoning models by checking consistency under geometric transformations, rather than requiring labeled training data. The approach uses consistency verifiers—reward functions that validate geometric and semantic coherence—combined with a new optimal transport-based policy optimization strategy called OT-GRPO. The method achieves performance comparable to supervised fine-tuning while eliminating the need for ground-truth annotations, potentially reducing the cost and effort required to improve AI spatial reasoning capabilities.
A new study from arXiv proposes that large reasoning models already possess spatial reasoning capabilities but lack proper alignment, rather than suffering from knowledge deficits. The researchers developed a self-supervised reinforcement learning framework that uses consistency verifiers—reward functions checking for geometric and semantic consistency under transformations—to train models without ground-truth labels. The approach combines image transformations (like flipping) and textual transformations (like reordering objects) with a novel optimal transport-based reinforcement learning strategy called OT-GRPO, designed for pairwise verifiers. According to the abstract, this label-free consistency training achieves accuracy comparable to supervised models trained with ground-truth annotations and generalizes well across diverse tasks and data domains. This work addresses a significant gap in current large reasoning models' spatial reasoning performance while reducing annotation requirements.
What's missing
The study's limitations and open questions are not detailed in the abstract provided. Specific benchmark datasets used, quantitative performance metrics, and comparisons with other unsupervised or semi-supervised spatial reasoning approaches are not included. The computational cost and training efficiency of OT-GRPO relative to supervised methods are not discussed.
What different sources said
- arXiv cs.AICenter
The Art of Interrogation: Consistency Amplifies Factuality in Spatial Reasoning
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