SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning
SkillRL enhances performance by 15.3% in tasks like ALFWorld via recursive skill-augmented reinforcement learning.
Key Findings
Methodology
SkillRL employs an experience-based skill distillation mechanism to construct a hierarchical skill library, SkillBank, and uses adaptive retrieval strategies and recursive evolution to co-evolve the skill library with the agent's policy during reinforcement learning. This approach significantly reduces token footprint while enhancing reasoning utility.
Key Results
- On ALFWorld, SkillRL achieved a success rate of 89.9%, improving by 12.3% over GRPO.
- In the WebShop task, SkillRL achieved a success rate of 72.7%, significantly outperforming baseline methods.
- In seven search-augmented tasks, SkillRL achieved an average score of 47.1%, surpassing EvolveR's 43.1%.
Significance
SkillRL addresses the redundancy and noise issues in existing methods through automatic skill discovery and recursive evolution, significantly enhancing the generalization capabilities of large language models in complex tasks. This framework offers new insights for reinforcement learning, especially in scenarios requiring efficient experience transfer.
Technical Contribution
SkillRL introduces experience distillation and recursive evolution mechanisms, offering more efficient skill abstraction and policy optimization compared to existing methods. This approach not only improves task success rates but also enhances model reasoning capabilities and robustness.
Novelty
SkillRL is the first to combine skill discovery with recursive evolution, forming a dynamically evolving skill library that overcomes the limitations of traditional experience storage methods, providing a more efficient experience transfer mechanism.
Limitations
- In some complex tasks, the construction and updating of the skill library may lead to increased computational overhead.
- The accuracy of skill retrieval depends on the quality of task descriptions.
Future Work
Future research could explore more efficient skill retrieval algorithms and applications on larger-scale tasks to further enhance the performance and applicability of SkillRL.
AI Executive Summary
SkillRL significantly enhances the performance of large language models in complex tasks through recursive skill-augmented reinforcement learning. Existing methods often store raw trajectories, leading to redundancy and noise issues, hindering the extraction of high-level behavioral patterns. SkillRL bridges this gap by introducing an experience-based skill distillation mechanism to construct a hierarchical skill library, SkillBank, and employing adaptive retrieval strategies and recursive evolution to co-evolve the skill library with the agent's policy during reinforcement learning.
Experimental results on ALFWorld, WebShop, and seven search-augmented tasks demonstrate that SkillRL achieves state-of-the-art performance, outperforming strong baselines by 15.3% and maintaining robustness as task complexity increases. The innovation of SkillRL lies in its automatic skill discovery and recursive evolution mechanisms, addressing the redundancy and noise issues in existing methods, significantly enhancing the generalization capabilities of large language models in complex tasks.
While SkillRL performs excellently in multiple tasks, the construction and updating of the skill library may lead to increased computational overhead in some complex tasks. Future research could explore more efficient skill retrieval algorithms and applications on larger-scale tasks to further enhance the performance and applicability of SkillRL.
Deep Analysis
Background
In recent years, large language models have demonstrated remarkable capabilities in complex tasks, yet they often operate in isolation, failing to learn from past experiences. Existing memory-based methods primarily store raw trajectories, which are often redundant and noisy, preventing the extraction of high-level behavioral patterns.
Core Problem
Large language models often fail to effectively utilize past experiences in complex tasks. Existing methods store raw trajectories that are lengthy and noisy, making it difficult to extract critical information, thus hindering model generalization in new tasks.
Innovation
SkillRL combines automatic skill discovery with recursive evolution to form a dynamically evolving skill library, overcoming the limitations of traditional experience storage methods and providing a more efficient experience transfer mechanism.
Methodology
- �� Experience Distillation Mechanism: Transforms diverse trajectories into structured skills.
- �� Hierarchical Skill Library: Differentiates between general and task-specific skills.
- �� Recursive Evolution Mechanism: Dynamically updates the skill library during reinforcement learning.
Experiments
Experiments were conducted on ALFWorld, WebShop, and seven search-augmented tasks, comparing SkillRL's performance with various baseline methods, using success rates and average scores as primary evaluation metrics.
Results
SkillRL achieved a success rate of 89.9% on ALFWorld, 72.7% on WebShop, and an average score of 47.1% in search-augmented tasks, all significantly outperforming baseline methods.
Applications
SkillRL is applicable in complex tasks requiring efficient experience transfer, such as smart home control and autonomous driving, significantly improving task success rates and model robustness.
Limitations & Outlook
In some complex tasks, the construction and updating of the skill library may lead to increased computational overhead, and the accuracy of skill retrieval depends on the quality of task descriptions.
Plain Language Accessible to non-experts
Imagine a chef working in a kitchen. Traditional methods are like the chef starting from scratch every time they cook, while SkillRL is like a cookbook that records various recipes. Each time the chef cooks, they just need to consult the cookbook to quickly find the necessary steps and techniques. This not only saves time but also increases the success rate of the dishes. SkillRL breaks down complex tasks into reusable skills, much like breaking down complex dishes into simple steps, allowing the model to complete tasks more efficiently.
ELI14 Explained like you're 14
Imagine playing a game where you have to learn how to pass each level from scratch every time. That would be exhausting, right? SkillRL is like a super guidebook that summarizes each level's experience into simple tricks. Next time you face a similar level, you can easily pass it by looking at the guide. It's like learning math in school; with formulas and problem-solving techniques, solving problems becomes much easier. SkillRL allows large language models to handle complex tasks as easily as you use a guidebook!
Glossary
Reinforcement Learning
A machine learning method that learns optimal policies by interacting with the environment and receiving rewards.
SkillRL uses reinforcement learning to achieve recursive evolution of the skill library.
Skill Distillation
Extracting reusable skills from complex experiences to reduce redundancy and noise.
SkillRL uses skill distillation to construct a hierarchical skill library.
Recursive Evolution
Dynamically updating the skill library during reinforcement learning to co-evolve with the agent's policy.
SkillRL enhances model adaptability through recursive evolution.
SkillBank
A hierarchical structure containing general and task-specific skills for guiding decision-making.
A core component of SkillRL for storing and retrieving skills.
Large Language Model
A deep learning-based model capable of understanding and generating natural language.
SkillRL implements skill augmentation in large language models.
Open Questions Unanswered questions from this research
- 1 How to efficiently apply SkillRL to larger-scale tasks? Current skill retrieval algorithms may face bottlenecks in complex tasks.
- 2 How to further reduce the computational overhead of the skill library? In some complex tasks, the construction and updating of the skill library may lead to increased computational overhead.
Applications
Immediate Applications
Smart Home Control
SkillRL can be used in smart home systems to improve automation and intelligence by learning user habits.
Long-term Vision
Autonomous Driving
Through SkillRL's skill enhancement mechanism, autonomous driving systems can better adapt to complex road conditions, improving driving safety and efficiency.
Abstract
Large Language Model (LLM) agents have shown stunning results in complex tasks, yet they often operate in isolation, failing to learn from past experiences. Existing memory-based methods primarily store raw trajectories, which are often redundant and noise-heavy. This prevents agents from extracting high-level, reusable behavioral patterns that are essential for generalization. In this paper, we propose SkillRL, a framework that bridges the gap between raw experience and policy improvement through automatic skill discovery and recursive evolution. Our approach introduces an experience-based distillation mechanism to build a hierarchical skill library SkillBank, an adaptive retrieval strategy for general and task-specific heuristics, and a recursive evolution mechanism that allows the skill library to co-evolve with the agent's policy during reinforcement learning. These innovations significantly reduce the token footprint while enhancing reasoning utility. Experimental results on ALFWorld, WebShop and seven search-augmented tasks demonstrate that SkillRL achieves state-of-the-art performance, outperforming strong baselines over 15.3% and maintaining robustness as task complexity increases. Code is available at this https://github.com/aiming-lab/SkillRL.