Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models

Researchers introduce Ariadne, a framework demonstrating that Reinforcement Learning with Verifiable Rewards (RLVR) expands spatial reasoning capabilities in Vision-Language Models beyond their base distribution. Testing on synthetic mazes and real-world navigation benchmarks shows the technique enables models to solve previously unsolvable problems, suggesting genuine capability expansion rather than sampling efficiency.

The research challenges prevailing assumptions about RLVR's limitations in expanding model capabilities. While prior studies suggested RLVR merely amplifies existing behaviors in language models, this work reveals the technique may fundamentally extend reasoning boundaries in vision-language domains—a significant distinction that reshapes understanding of AI capability development.

The Ariadne framework's controlled environment provides rigorous testing grounds where difficulty scales precisely with path complexity. The base model's consistent 0% accuracy on harder problems, despite increased sampling attempts, establishes a clear capability ceiling that RLVR subsequently breaks through. This methodological rigor addresses previous criticisms that capability claims lacked controlled validation.

The zero-shot transfer to MapBench and ReasonMap benchmarks carries substantial implications. Models trained exclusively on synthetic data demonstrating improved performance on real-world navigation tasks indicates the learned spatial reasoning generalizes beyond training distribution specifics. This challenges the hypothesis that improvements stem from distribution-specific overfitting.

For the broader AI development landscape, these findings suggest RLVR represents a more powerful optimization technique than previously recognized, particularly for spatial and visual reasoning tasks. As vision-language models increasingly power autonomous systems and robotics applications, capability expansion methodologies become commercially significant. The research validates that systematic reward structures can unlock new problem-solving dimensions rather than merely refinancing existing ones. Future work examining whether similar expansion occurs in other reasoning domains could determine whether this represents a general principle of RLVR's potential.

• →RLVR successfully extends spatial reasoning boundaries in VLMs, solving problems unsolvable by base models even with increased sampling.
• →Synthetic maze training transfers effectively to real-world navigation benchmarks in zero-shot settings, demonstrating genuine capability expansion.
• →The research contradicts prior assumptions that RLVR only amplifies pre-training behaviors rather than creating new capabilities.
• →Ariadne's controlled framework enables precise difficulty regulation, providing rigorous methodology for measuring capability expansion.
• →Findings suggest RLVR's potential extends beyond language domains to visual reasoning, with implications for autonomous systems development.