Skill Expansion and Composition in Parameter Space (2502.05932v2)

Abstract: Humans excel at reusing prior knowledge to address new challenges and developing skills while solving problems. This paradigm becomes increasingly popular in the development of autonomous agents, as it develops systems that can self-evolve in response to new challenges like human beings. However, previous methods suffer from limited training efficiency when expanding new skills and fail to fully leverage prior knowledge to facilitate new task learning. In this paper, we propose Parametric Skill Expansion and Composition (PSEC), a new framework designed to iteratively evolve the agents' capabilities and efficiently address new challenges by maintaining a manageable skill library. This library can progressively integrate skill primitives as plug-and-play Low-Rank Adaptation (LoRA) modules in parameter-efficient finetuning, facilitating efficient and flexible skill expansion. This structure also enables the direct skill compositions in parameter space by merging LoRA modules that encode different skills, leveraging shared information across skills to effectively program new skills. Based on this, we propose a context-aware module to dynamically activate different skills to collaboratively handle new tasks. Empowering diverse applications including multi-objective composition, dynamics shift, and continual policy shift, the results on D4RL, DSRL benchmarks, and the DeepMind Control Suite show that PSEC exhibits superior capacity to leverage prior knowledge to efficiently tackle new challenges, as well as expand its skill libraries to evolve the capabilities. Project website: https://ltlhuuu.github.io/PSEC/.

• The paper introduces a novel framework (PSEC) that leverages parameter-efficient fine-tuning with LoRA modules to expand and compose skills.
• The paper employs context-aware activation techniques to dynamically integrate skill primitives for versatile autonomous learning.
• The paper demonstrates superior performance on benchmarks like D4RL and DeepMind Control Suite, proving enhanced adaptability and learning efficiency.

Skill Expansion and Composition in Parameter Space: A Comprehensive Analysis

The paper "Skill Expansion and Composition in Parameter Space" introduces a novel framework, Parametric Skill Expansion and Composition (PSEC), designed to enhance the learning capabilities and adaptability of autonomous agents. This framework addresses key limitations observed in traditional decision-making algorithms, particularly those adhering to a tabula rasa approach. Through strategic innovations, the PSEC framework aims to leverage prior knowledge for efficient skill acquisition and expansion.

Core Contributions and Methodological Advances

The PSEC framework's core innovation lies in its reflection of human-like problem-solving abilities, where existing knowledge is reused to solve new tasks. The methodology pivots around the development of a manageable skill library through parameter-efficient fine-tuning. The Low-Rank Adaptation (LoRA) modules are central to this approach, facilitating skill primitives' integration for efficient and flexible skill expansion. Importantly, these modules enable the direct composition of skills in parameter space by merging LoRA modules, making optimal use of shared information across skills.

Key contributions include:

• Parameterized Skill Composition: PSEC introduces a novel method of combining skills at a parameter level, providing flexibility and expressiveness in programming new skills.
• Context-aware Skill Activation: A context-aware module is proposed to dynamically activate skills in response to task requirements, further mimicking adaptive human problem-solving.
• Applications Across Scenarios: The framework demonstrates its efficacy across multiple applications, including multi-objective composition, dynamics shift, and continual policy shift, verified through benchmarks like D4RL and the DeepMind Control Suite.

Strong Numerical Results and Implications

The paper reports that PSEC exhibits superior capability in leveraging prior knowledge to tackle new challenges efficiently, as evidenced by performance metrics in several benchmarks. For instance, the results demonstrate that PSEC outperforms traditional methods in settings like dynamic shifts and policy continuation, which are crucial for real-world applications where evolving tasks frequently occur.

Implications for Future AI Development

The theoretical and practical implications of this research are significant. The ability to inherently manage and expand a skill library using parameterized techniques like LoRA positions PSEC as an influential framework for the development of autonomous systems aiming for versatile and adaptive learning capabilities. Furthermore, the success of PSEC in adaptive environments suggests the potential for future AI systems to exhibit a level of flexibility and continuous improvement more akin to biological intelligence.

Speculations on Future Developments

As AI continues to evolve, frameworks like PSEC could form the foundation for more advanced adaptive learning systems. The modular and flexible nature of parameter-level skill composition may inspire further research into more granular and efficient learning methods. Such advancements could lead to breakthroughs in autonomous driving, robotics, and other fields where adaptive intelligence is paramount.

Concluding Remarks

In conclusion, this paper provides a robust framework for advancing the capabilities of autonomous agents by leveraging prior knowledge and parameter-efficient techniques. The substantial empirical results and theoretical innovations position PSEC as a transformative approach for efficient skill expansion and composition in AI, holding great promise for future developments in the field.