SVoT: State-aware Visualization-of-Thought for Spatial Reasoning via Reinforcement Learning
Quick Answer
This paper shows that The State-aware Visualization-of-Thought (SVoT) framework enhances spatial reasoning in Multimodal Large Language Models (MLLMs) by generating verifiable intermediate states and visualizations.
Quick Take
Trained via (GRPO), SVoT achieves state-of-the-art performance with up to 65% accuracy gain on out-of-distribution test sets across five newly established domains, including Pacman and Gather.
Key Points
- SVoT integrates transition reasoning chains for improved multi-hop spatial reasoning.
- The framework generates interleaved, verifiable intermediate states and visualizations.
- Five domains established for systematic evaluation include novel environments like Pacman.
- SVoT achieves state-of-the-art performance with a 65% accuracy gain on specific test sets.
- Group Relative Policy Optimization (GRPO) is used for effective training and reward design.
Paper Resources
Source Excerpt
arXiv:2606. 11770v1 Announce Type: new Abstract: Spatial reasoning remains a challenge for Multimodal (MLLMs), as it requires reliable multi-hop inference over both intermediate states and state transitions. Current studies often leave intermediate states unverified and treat state transitions as implicit processes, which limits reliability in multi-hop spatial reasoning.
To address this, we propose State-aware Visualization-of-Thought (SVoT), a reinforcement learning framework that generates interleaved, verifiable intermediate states and visualizations. …
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