SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals (AI summary)

Key Points

1. Language agents powered by large language models (LLMs) often face challenges in achieving high-level goals without detailed instructions and in adapting to environments where feedback is delayed.

2. In response to these challenges, the paper introduces S ELF G OAL, a novel automatic approach designed to enhance agents’ capabilities to achieve high-level goals with limited human prior and environmental feedback. The core concept of S ELF G OAL involves adaptively breaking down a high-level goal into a tree structure of more practical subgoals during interaction with environments while identifying the most useful subgoals and progressively updating this structure.

3. The paper presents experimental results that demonstrate that S ELF G OAL significantly enhances the performance of language agents across various tasks, including competitive, cooperative, and deferred feedback environments.

4. Previous works have focused on creating two types of auxiliary guidance for language agents to achieve high-level goals: prior task decomposition and post-hoc experience summarization.

5. The proposed S ELF G OAL framework combines the best of both worlds by dynamically decomposing the task and its high-level goal during interaction with the environment. This approach allows the agent to build and use guidelines that vary in detail and aspect.

6. S ELF G OAL is featured with two main modules to operate a G OALT REE:
- Search Module: tasked with selecting the top-K most suited nodes of goals based on the provided current state and existing nodes in G OALT REE.
- Decomposition Module: responsible for breaking down a goal node into a list of more concrete subgoals as subsequent leaves, ensuring an adaptive self-growth of G OALT REE.

7. The experiments conducted in both collaborative and competitive scenarios demonstrate that S ELF G OAL provides precise guidance for high-level goals and adapts to diverse environments, significantly improving language agent performance. The experimental tasks include Public Goods Games, Guess 2/3 of the Average games, First-price Auctions, and Bargaining scenarios.

8. The paper discusses how the granularity of guidelines in G OALT REE affects task solving and how the quality of G OALT REE affects goal achievement. The results show that a balanced, adaptive depth of the guidance tree is crucial to mitigate the drawbacks of overly detailed guidance and that higher-quality G OALT REEs significantly boost the performance of S ELF G OAL.

9. S ELF G OAL was found to improve the rationality in agents’ behaviors, with agents equipped with S ELF G OAL consistently displaying more rational behavior and achieving quicker convergence to the Nash equilibrium in repeated games. Furthermore, the paper includes a case study using Mistral-7B in a bargaining game, comparing the performance of agents using S ELF G OAL with those using alternative methods, demonstrating the advantages of S ELF G OAL in providing actionable guidance and promoting rational behavior in the agents.

Summary

The research paper introduces the SELFGOAL approach, a novel automatic approach designed to enhance the capabilities of language agents powered by large language models (LLMs) to achieve high-level goals with limited human prior and environmental feedback. The core concept of SELFGOAL involves adaptively breaking down a high-level goal into a tree structure of more practical subgoals during the interaction with environments while identifying the most useful subgoals and progressively updating this structure. The paper demonstrates that SELFGOAL significantly enhances the performance of language agents across various tasks, including competitive, cooperative, and deferred feedback environments.

The paper presents the challenges faced by language agents powered by comprehensive language models in achieving high-level goals without detailed instructions and in adapting to environments where feedback is delayed. The authors propose SELFGOAL as a solution to enable autonomous language agents to consistently achieve high-level goals without the need for frequent retraining. The approach involves dynamically decomposing the task and its high-level goal during interaction with the environment to adapt to diverse environments.

SELFGOAL operates using a G OALT REE, a tree of textual subgoals, with two main modules: the Search Module, which selects the top-K most suited nodes of goals based on the provided current state and existing nodes in G OALT REE, and the Decomposition Module, which breaks down a goal node into a list of more concrete subgoals as subsequent leaves, ensuring an adaptive self-growth of G OALT REE. Extensive experiments in various competition and collaboration scenarios show that SELFGOAL provides precise guidance for high-level goals and adapts to diverse environments, significantly improving language agent performance. The experiments include the analysis of scenarios, such as the Public Goods Game, Guess 2/3 of the Average, First-price Auction, and Bargaining, which demonstrate the effectiveness of SELFGOAL across various dynamic tasks.

The paper also provides details about the use of large language models (LLMs) for decision making and highlights that SELFGOAL enhances the effectiveness of LLM-based agents by dynamically decomposing high-level goals into subgoals and adapting to diverse environments without the need for frequent retraining. Additionally, the paper delves into an in-depth comparison of SELFGOAL with other agent frameworks and presents detailed results and performance evaluations across various scenarios and baseline frameworks. It also includes an ablation study and explores the influence of the granularity and quality of the G OALT REE on goal achievement and rational behavior of agents.

Furthermore, a case study is presented, illustrating how agents from different frameworks reason and plan in a dynamic environment, and how SELFGOAL provides actionable guidance leading to improved performance. The authors conclude by highlighting the effectiveness of SELFGOAL in enhancing the capabilities of language agents, with potential for further improvements in understanding and summarizing capabilities of LLMs.

Reference: https://arxiv.org/abs/2406.04784