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About this paper
Ru Zhang, Weijie Qiu
SPyCE introduces a framework for multimodal agents that distills reusable hierarchical skills (execution and workflow) from successful interaction trajectories, which then co-evolve with the agent's policy. This closed-loop mechanism improves task success rates and tool-use efficiency across various multimodal reasoning benchmarks.
Key insights
- A hierarchical skill structure, comprising both execution and workflow skills, provides complementary guidance for agents, addressing both fine-grained local operations and high-level strategic planning.
- Continuously evolving the skill library with high-quality trajectories, rather than using a static memory, is crucial for sustained improvement and adaptation of the agent's policy.
- Distilling structured, reusable knowledge directly from successful multimodal interaction patterns leads to a more efficient and stable learning process compared to scalar reward-based or static memory approaches.
Summary by alphaXiv.
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