XSkill: Continual Learning from Experience and Skills in Multimodal Agents
Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still suffer from inefficient tool use and inflexible orchestration in open-ended settings. A central challenge is enabling such agents to continually improve without parameter updates by learning from past trajectories. We identify two complementary forms of reusable knowledge essential for this goal: experiences, providing concise action-level guidance for tool selection and decision making, and skills, providing structured task-level guidance for planning and tool use. To this end, we propose XSkill, a dual-stream framework for continual learning from experience and skills in multimodal agents. XSkill grounds both knowledge extraction and retrieval in visual observations. During accumulation, XSkill distills and consolidates experiences and skills from multi-path rollouts via visually grounded summarization and cross-rollout critique. During inference, it retrieves and adapts this knowledge to the current visual context and feeds usage history back into accumulation to form a continual learning loop. Evaluated on five benchmarks across diverse domains with four backbone models, XSkill consistently and substantially outperforms both tool-only and learning-based baselines. Further analysis reveals that the two knowledge streams play complementary roles in influencing the reasoning behaviors of agents and show superior zero-shot generalization.
69b3c62e2ad6c5d4cf486c55
Guanyu Jiang
69b3c62e2ad6c5d4cf486c56
Zhaochen Su
claimed_verified
2026-03-17T09:37:54.166Z
64264095ba51f8a2136946a0
https://cdn-avatars.huggingface.co/v1/production/uploads/64264095ba51f8a2136946a0/FR33boVpkDXcrvGMBmprF.jpeg
Zhaochen Su
Warrieryes
user
Warrieryes
69b3c62e2ad6c5d4cf486c57
Xiaoye Qu
69b3c62e2ad6c5d4cf486c58
Yi R.
69b3c62e2ad6c5d4cf486c59
Fung
XSkill: Continual Learning from Experience and Skills in Multimodal Agents
Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still suffer from inefficient tool use and inflexible orchestration in open-ended settings. A central challenge is enabling such agents to continually improve without parameter updates by learning from past trajectories. We identify two complementary forms of reusable knowledge essential for this goal: experiences, providing concise action-level guidance for tool selection and decision making, and skills, providing structured task-level guidance for planning and tool use. To this end, we propose XSkill, a dual-stream framework for continual learning from experience and skills in multimodal agents. XSkill grounds both knowledge extraction and retrieval in visual observations. During accumulation, XSkill distills and consolidates experiences and skills from multi-path rollouts via visually grounded summarization and cross-rollout critique. During inference, it retrieves and adapts this knowledge to the current visual context and feeds usage history back into accumulation to form a continual learning loop. Evaluated on five benchmarks across diverse domains with four backbone models, XSkill consistently and substantially outperforms both tool-only and learning-based baselines. Further analysis reveals that the two knowledge streams play complementary roles in influencing the reasoning behaviors of agents and show superior zero-shot generalization.
Multimodal agents using XSkill framework achieve improved continual learning through dual-stream knowledge extraction and retrieval from visual observations, enhancing tool use efficiency and reasoning capabilities.
multimodal agents continual learning tool selection skill learning experience learning visual observations multi-path rollouts visually grounded summarization cross-rollout critique inference knowledge retrieval continual learning loop zero-shot generalization