Ariadne: A Controllable Framework for Probing and Extending VLM
  Reasoning Boundaries
While Vision-Language Models (VLMs) post-trained with Reinforcement Learning
(RL) show impressive general reasoning, their evaluation is often confined to
language-dominant tasks (e.g., math). This raises a critical question: can RL
post-training truly extend the inherent capability boundary of a base VLM,
particularly for visual-centric spatial tasks where it initially fails? To
investigate this, we introduce Ariadne, a framework utilizing synthetic mazes
for multi-step spatial reasoning where task difficulty (e.g., path length,
turns) is precisely controlled. We leverage this controllable environment to
train VLMs using Reinforcement Learning with Verified Rewards (RLVR) in a
difficulty-aware curriculum. Surprisingly, post-RLVR training, the VLM achieves
over 50% accuracy on a problem set where the base model scored 0%,
demonstrating that our approach expands the model's initial capability
boundary. To assess real-world viability, we evaluate out-of-distribution (OOD)
generalization on practical benchmarks. Despite training only on synthetic maze
samples, Ariadne achieves significant zero-shot improvements, averaging 16% on
MapBench (e.g., museum navigation) and 24% on ReasonMap (subway transfer
tasks). These results confirm that our method not only broadens the model's
fundamental limits but also enhances its generalization to real-world spatial
reasoning. We acknowledge our study is limited to the post-training phase,
given the opaqueness of pre-training data, and hope our research motivates
further work on specialized, capability-extending alignment.
690ba1d560494e4fa767559d
Minghe Shen
690ba1d560494e4fa767559e
Zhuo Zhi
690ba1d560494e4fa767559f
Chonghan Liu
690ba1d560494e4fa76755a0
Shuo Xing
690ba1d560494e4fa76755a1
Zhengzhong Tu
690ba1d560494e4fa76755a2
Che Liu
Ariadne: A Controllable Framework for Probing and Extending VLM
  Reasoning Boundaries
While Vision-Language Models (VLMs) post-trained with Reinforcement Learning
(RL) show impressive general reasoning, their evaluation is often confined to
language-dominant tasks (e.g., math). This raises a critical question: can RL
post-training truly extend the inherent capability boundary of a base VLM,
particularly for visual-centric spatial tasks where it initially fails? To
investigate this, we introduce Ariadne, a framework utilizing synthetic mazes
for multi-step spatial reasoning where task difficulty (e.g., path length,
turns) is precisely controlled. We leverage this controllable environment to
train VLMs using Reinforcement Learning with Verified Rewards (RLVR) in a
difficulty-aware curriculum. Surprisingly, post-RLVR training, the VLM achieves
over 50% accuracy on a problem set where the base model scored 0%,
demonstrating that our approach expands the model's initial capability
boundary. To assess real-world viability, we evaluate out-of-distribution (OOD)
generalization on practical benchmarks. Despite training only on synthetic maze
samples, Ariadne achieves significant zero-shot improvements, averaging 16% on
MapBench (e.g., museum navigation) and 24% on ReasonMap (subway transfer
tasks). These results confirm that our method not only broadens the model's
fundamental limits but also enhances its generalization to real-world spatial
reasoning. We acknowledge our study is limited to the post-training phase,
given the opaqueness of pre-training data, and hope our research motivates
further work on specialized, capability-extending alignment.
Ariadne, a framework using synthetic mazes and RLVR, expands VLMs' capability in visual-centric spatial reasoning and improves zero-shot generalization on real-world benchmarks.
Vision-Language Models Reinforcement Learning RLVR synthetic mazes multi-step spatial reasoning difficulty-aware curriculum out-of-distribution generalization MapBench ReasonMap