Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models
Summary: arXiv:2511.00710v4 Announce Type: replace
Abstract: Recent studies posit that Reinforcement Learning with Verifiable Rewards (RLVR) primarily amplifies behaviors inherent to the pre-training distribution rather than inducing new capabilities, but these insights are predominantly limited to language-only domains, leaving the dynamics of visual-centric spatial reasoning under-explored.
To examine the impact of RLVR on the capability boundaries of Vision-Language Models (VLMs), we introduce Ariadne, a controlled framework based on synthetic maze navigation where the reasoning difficulty is precisely regulated by path length and the number of turns.
Key Findings
Our research reveals significant insights into the capabilities of VLMs when enhanced by RLVR:
- Extended Spatial Reasoning Boundaries: The application of RLVR has been shown to extend the spatial reasoning capabilities of the VLMs. This is evidenced by achieving successful navigation outcomes on complex problems where the base policy VLM recorded a consistent accuracy of 0%, even as the pass@k sampling budgets increased.
- Navigation in Synthetic Mazes: Despite being trained solely on synthetic mazes, the optimized policy demonstrates a marked ability to navigate previously unreachable search spaces, indicating a genuine enhancement in reasoning capabilities.
- Zero-Shot Evaluation: We further evaluated the model’s performance on two real-world navigation benchmarks, MapBench and ReasonMap, in a zero-shot setting. The results indicated improvements in these out-of-domain tasks, bolstering our hypothesis that RLVR facilitates true spatial reasoning capability expansion.
Implications of the Research
The findings from our study have several implications for the future of AI and machine learning, particularly in the realm of Vision-Language Models:
- Broader Applications: The enhanced capabilities of VLMs could be leveraged in various applications, including robotics, autonomous navigation, and complex decision-making tasks.
- Advancing AI Understanding: Understanding how RLVR impacts reasoning capabilities could inform the development of more advanced AI systems capable of tackling complex, real-world challenges.
- Foundation for Future Research: Our work lays the groundwork for further exploration into the dynamics of visual-centric reasoning and the potential of RLVR in other domains beyond language.
Conclusion
In summary, our investigation into the effects of Reinforcement Learning with Verifiable Rewards on Vision-Language Models using the Ariadne framework has uncovered promising advancements in spatial reasoning capabilities. The evidence suggests that RLVR not only amplifies existing behaviors but also fosters the development of new ones, particularly in visual reasoning tasks. As AI continues to evolve, these insights will be essential for shaping future research and applications in the field.