Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments (2501.10893v1)

Abstract: Autonomous agents powered by LLMs have the potential to enhance human capabilities, assisting with digital tasks from sending emails to performing data analysis. The abilities of existing LLMs at such tasks are often hindered by the lack of high-quality agent data from the corresponding environments they interact with. We propose Learn-by-interact, a data-centric framework to adapt LLM agents to any given environments without human annotations. Learn-by-interact synthesizes trajectories of agent-environment interactions based on documentations, and constructs instructions by summarizing or abstracting the interaction histories, a process called backward construction. We assess the quality of our synthetic data by using them in both training-based scenarios and training-free in-context learning (ICL), where we craft innovative retrieval approaches optimized for agents. Extensive experiments on SWE-bench, WebArena, OSWorld and Spider2-V spanning across realistic coding, web, and desktop environments show the effectiveness of Learn-by-interact in various downstream agentic tasks -- baseline results are improved by up to 12.2\% for ICL with Claude-3.5 and 19.5\% for training with Codestral-22B. We further demonstrate the critical role of backward construction, which provides up to 14.0\% improvement for training. Our ablation studies demonstrate the efficiency provided by our synthesized data in ICL and the superiority of our retrieval pipeline over alternative approaches like conventional retrieval-augmented generation (RAG). We expect that Learn-by-interact will serve as a foundation for agent data synthesis as LLMs are increasingly deployed at real-world environments.

• The paper introduces a framework that uses agent-environment interactions to autonomously generate high-quality data, reducing reliance on human annotations.
• It employs backward construction to retrofit task instructions from LLM-generated trajectories, enhancing data alignment and model performance.
• Experimental results across multiple benchmarks showed improvements up to 12.2% in ICL and 19.5% in training, demonstrating the framework's robust adaptability.

Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments

The paper, "Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments," presents a framework to enhance the adaptability of LLM agents within diverse environments. The core idea revolves around synthesizing agent-specific data autonomously through agent-environment interactions, thereby removing the dependency on costly human annotations.

Overview and Methodology

The paper introduces Learn-by-interact, a framework designed to generate high-quality agentic data without human involvement. The framework leverages existing environment-related resources such as documentations and tutorials to synthesize task instructions. To address the typical misalignment between these instructions and the trajectories generated by LLMs, the authors propose a concept called backward construction. This involves the retrospective creation of new instructions based on the generated trajectories, aiming to match the actual agent-environment interactions closely.

The synthesized data, once generated, is used in both In-Context Learning (ICL) and training-based scenarios to enhance model performance. For ICL, the paper introduces agentic retrieval, blending observation-based and model-based approaches to effectively utilize synthesized data. In training-based setups, the synthesized data serves directly as training material.

Experimental Results

The researchers conducted extensive experiments across various benchmarks including SWE-bench, WebArena, OSWorld, and Spider2-V, which cover coding, web, and desktop environments. The results demonstrated notable improvements over baseline approaches, including enhancements up to 12.2% and 19.5% in ICL and training scenarios, respectively, with Claude-3.5 and Codestral-22B.

The experimental findings indicate that the synthesized data significantly boosts the performance of LLMs, surpassing both human-annotated baselines and alternative synthesized approaches. Backward construction, in particular, plays a critical role by not only increasing data volume but also improving data quality.

Implications and Future Directions

The framework's implications are substantial for advancing the autonomous adaptation of LLMs in realistic environments, reducing the reliance on human-labeled datasets. This approach presents a scalable solution for deploying LLMs across varied environments by enhancing their situational understanding and adaptability through interaction-driven data synthesis.

The paper paves the way for future research exploring more efficient ways of synthesizing data, potentially integrating multi-modal capabilities and extending the approach to scenarios like robotics and other domains requiring intricate environment interactions.

Conclusion

"Learn-by-interact" represents a significant stride in reducing the human annotation burden associated with agent data. By synthesizing data through environment interactions and employing innovative retrieval methods in ICL, this framework offers a robust solution for facilitating the adaptability of LLMs. This approach hints at promising directions for deploying intelligent agents in practical applications, impacting areas from autonomous digital assistants to complex task-oriented systems.