AI

ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills

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Briefings

ViSkill is a visual-native skill learning framework for VLM agents that encodes successful interactions as composite visual skill cards, creating a closed feedback loop between skill accumulation and policy improvement. Evaluated on Sokoban, FrozenLake, and PrimitiveSkill, the method achieves an overall success rate of 0.89, rising to 0.91 with cold-start initialization, outperforming all evaluated proprietary and open-source baselines while converging faster than standard PPO.

Key points

• ViSkill encodes successful interactions as composite visual skill cards directly accessible to VLM agents.
• ViSkill achieves an overall success rate of 0.89 on Sokoban, FrozenLake, and PrimitiveSkill, rising to 0.91 with cold-start initialization.

Context

Skill-augmented agents improve sample efficiency by distilling successful trajectories into reusable strategies. ViSkill addresses the limitation that existing approaches remain text-centric, linearizing spatial layouts and action-state correspondences into language that loses critical geometric structure.

Limitations

Evaluation is limited to three specific environments (Sokoban, FrozenLake, PrimitiveSkill). The study does not provide results for other visual environments or long-term stability under complex dynamic conditions. The success rate figures represent the highest reported performance for each environment, but the study does not claim superiority over all possible future environments.

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