arXiv:2605.30912v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) improves vision-language models (VLMs) by optimizing outcome rewards derived from final answers. However, such outcome-only rewards do not tell the model which image regions justify an answer. For questions that require visual grounding, these rewards cannot distinguish responses supported by relevant visual evidence from those produced by language-prior shortcuts or lucky guesses. We introduce EASE (Evidence-Anchored Spatial Attention), which augments multimodal RLVR with visual-evidence pr
The paper addresses a critical limitation in current multimodal reinforcement learning with verifiable rewards (RLVR) by proposing a method to better align AI models with human-understandable visual evidence, bridging current gaps in explainability and reliability.
Improved visual grounding in AI models like VLMs makes them more robust and trustworthy, moving beyond 'lucky guesses' and language priors to verifiable, evidence-based reasoning crucial for sensitive applications.
This development allows for more reliable and interpretable multimodal AI systems that can explicitly justify their decisions based on visual evidence, enhancing their utility in domains requiring high accuracy and auditability.
- · AI developers
- · Vision-language models (VLMs)
- · AI applications requiring explainability
- · Responsible AI initiatives
- · Black-box multimodal AI systems
- · AI models relying on shortcuts
Multimodal AI systems become more robust and interpretable due to improved visual grounding.
Increased adoption of multimodal AI in high-stakes fields like medical imaging or autonomous driving due to enhanced trustworthiness.
New regulatory frameworks may emerge to mandate evidence-anchored reasoning for AI systems, mirroring human accountability.
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Read at arXiv cs.CL