AlignEvoSkill: Towards Knowledge-Aware and Task-Aligned Agent Skill Evolution
AlignEvoSkill enhances skill evolution via knowledge tags and task alignment, achieving a 34.7% improvement.
Key Findings
Methodology
AlignEvoSkill combines knowledge-tag-guided generation and likelihood-based task alignment. It identifies task-relevant knowledge tags, retrieves complementary skills, generates candidate skills, and selects high-quality candidates based on knowledge coverage and task alignment scores.
Key Results
- AlignEvoSkill shows a 34.7% improvement over the non-evolution baseline across 3 benchmarks, achieving new SOTA in skill evolution with lower cost.
- Knowledge-tag coverage score improved by 9.9%, task alignment likelihood increased by 8.9%.
- Ablation studies show performance drops significantly when any component is removed.
Significance
AlignEvoSkill is significant for academia and industry, addressing issues of improper knowledge coverage and weak task alignment in existing methods, enhancing the long-term capabilities of LLM agents.
Technical Contribution
AlignEvoSkill uses knowledge tags to identify task-relevant requirements and retrieve complementary skills, applying a task-conditioned likelihood criterion to filter candidate skills, ensuring retained skills are both knowledge-complete and well-aligned with the target task.
Novelty
AlignEvoSkill is the first to combine knowledge tags and task alignment in skill evolution, significantly improving the effectiveness of knowledge coverage and task alignment compared to existing methods.
Limitations
- In some complex tasks, there may still be incomplete coverage of all knowledge tags.
- Task alignment likelihood evaluation may be sensitive to model choice.
Future Work
Future research can explore skill evolution in more complex task scenarios, optimize task alignment likelihood evaluation methods, and apply it to more models and datasets.
AI Executive Summary
In large language model agents, skill evolution is crucial for enhancing long-term capabilities. However, existing methods fall short in knowledge coverage and task alignment. AlignEvoSkill addresses this by combining knowledge-tag-guided generation and likelihood-based task alignment.
AlignEvoSkill first identifies task-relevant knowledge tags, retrieves complementary skills, and generates candidate skills. It then selects high-quality candidates based on knowledge coverage and task alignment scores. Experiments show that AlignEvoSkill achieves a 34.7% improvement over the non-evolution baseline across 3 benchmarks, setting a new SOTA in skill evolution with lower cost.
AlignEvoSkill is significant for academia and industry, addressing issues of improper knowledge coverage and weak task alignment in existing methods, enhancing the long-term capabilities of LLM agents. Future research can explore skill evolution in more complex task scenarios, optimize task alignment likelihood evaluation methods, and apply it to more models and datasets.
Deep Analysis
Background
Skill evolution is a key research direction for enhancing the long-term capabilities of large language model agents. Existing methods are mainly divided into skill-driven and trajectory-driven approaches but fall short in knowledge coverage and task alignment.
Core Problem
Existing skill evolution methods fall short in knowledge coverage and task alignment, resulting in evolved skills that may be incomplete or irrelevant.
Innovation
AlignEvoSkill combines knowledge-tag-guided generation and likelihood-based task alignment, addressing issues of knowledge coverage and task alignment for the first time in skill evolution.
Methodology
- �� Knowledge-tag-guided skill evolution: Identifies task-relevant requirements and retrieves complementary skills using knowledge tags.
- �� Likelihood-based task alignment: Applies a task-conditioned likelihood criterion to filter candidate skills, ensuring retained skills are both knowledge-complete and well-aligned with the target task.
Experiments
Experiments were conducted on 3 benchmarks and 4 large language models, showing that AlignEvoSkill achieves a 34.7% improvement over the non-evolution baseline, setting a new SOTA in skill evolution.
Results
AlignEvoSkill improves knowledge-tag coverage score by 9.9% and task alignment likelihood by 8.9%, significantly outperforming existing methods.
Applications
AlignEvoSkill can be used to enhance the performance of large language model agents in complex tasks, particularly in scenarios requiring high knowledge coverage and task alignment.
Limitations & Outlook
In some complex tasks, there may still be incomplete coverage of all knowledge tags. Task alignment likelihood evaluation may be sensitive to model choice.
Plain Language Accessible to non-experts
Imagine a factory where workers need different tools to complete tasks. The existing tool library might not be comprehensive or suitable for specific tasks. AlignEvoSkill acts like an intelligent tool management system that analyzes task requirements, finds suitable tools, and adjusts them as needed to ensure workers can complete tasks efficiently.
ELI14 Explained like you're 14
Imagine you're playing a puzzle-solving game. You have a toolbox, but some tools aren't quite right for the current puzzle. AlignEvoSkill is like a smart assistant that helps you pick and adjust tools, making it easier to solve the puzzle. Isn't that cool?
Glossary
Skill Evolution
The process of continuously updating and optimizing skill libraries to enhance the long-term capabilities of large language model agents.
AlignEvoSkill enhances agent performance through skill evolution.
Knowledge Tag
A short phrase describing the procedural knowledge contained in a skill, used to identify task-relevant requirements.
AlignEvoSkill uses knowledge tags to identify task requirements.
Task Alignment
Ensuring that evolved skills are highly relevant to the target task, providing effective guidance.
AlignEvoSkill selects high-quality skills through task alignment.
Large Language Model
A large-scale neural network model capable of understanding and generating natural language.
AlignEvoSkill is applied in large language model agents.
Benchmark
A standard dataset and task set used to evaluate model performance.
AlignEvoSkill is validated on multiple benchmarks.
Open Questions Unanswered questions from this research
- 1 How to achieve more efficient skill evolution in more complex tasks?
- 2 How to optimize task alignment likelihood evaluation methods?
Applications
Immediate Applications
Complex Task Optimization
AlignEvoSkill can be used to enhance the performance of large language model agents in complex tasks.
Long-term Vision
General AI
Continuously optimizing skill evolution to advance the development of general artificial intelligence.
Abstract
Reusable skills play a key role in improving LLM-based agents, but existing skill-evolution methods often fail to ensure that evolved skills both cover the knowledge required by the task and remain aligned with the target task. As a result, evolved skills could be incomplete or irrelevant. To address this limitation, we propose AlignEvoSkill, a skill-evolution framework that jointly models knowledge coverage and task alignment. Given failed task trajectories, AlignEvoSkill first identifies task-relevant knowledge tags, retrieves complementary prior skills, and adapts them into candidate skills that address missing knowledge. It then selects high-quality candidates using a joint filtering criterion based on knowledge-coverage and task-alignment scores. Experiments on 3 benchmarks with4 LLM backbones show a 34.7% relative gain of AlignEvoSkill over the non-evolution baseline and achieves a new SOTA in skill evolution with lower cost.