AgentRefine: Enhancing Agent Generalization through Refinement Tuning (2501.01702v1)

Abstract: LLM based agents have proved their ability to perform complex tasks like humans. However, there is still a large gap between open-sourced LLMs and commercial models like the GPT series. In this paper, we focus on improving the agent generalization capabilities of LLMs via instruction tuning. We first observe that the existing agent training corpus exhibits satisfactory results on held-in evaluation sets but fails to generalize to held-out sets. These agent-tuning works face severe formatting errors and are frequently stuck in the same mistake for a long while. We analyze that the poor generalization ability comes from overfitting to several manual agent environments and a lack of adaptation to new situations. They struggle with the wrong action steps and can not learn from the experience but just memorize existing observation-action relations. Inspired by the insight, we propose a novel AgentRefine framework for agent-tuning. The core idea is to enable the model to learn to correct its mistakes via observation in the trajectory. Specifically, we propose an agent synthesis framework to encompass a diverse array of environments and tasks and prompt a strong LLM to refine its error action according to the environment feedback. AgentRefine significantly outperforms state-of-the-art agent-tuning work in terms of generalization ability on diverse agent tasks. It also has better robustness facing perturbation and can generate diversified thought in inference. Our findings establish the correlation between agent generalization and self-refinement and provide a new paradigm for future research.

Enhancing Generalization in LLM Agents: The AgentRefine Approach

The paper "AgentRefine: Enhancing Agent Generalization through Refinement Tuning" addresses a critical challenge in leveraging LLMs for agent-based tasks: their limited generalization capability. Although LLMs have demonstrated proficiency in executing human-like complex tasks, the disparity between open-sourced models and proprietary models, such as the GPT series, is substantial. This work focuses on improving generalization in LLMs by proposing a novel training regime, termed "Refinement Tuning", through the introduction of the AgentRefine framework.

Background and Motivation

In recent advances, LLM-based agents have shown potential in automating complex real-world tasks across various domains. However, existing fine-tuning approaches emphasize performance on specific training environments, often resulting in models that overfit their learned settings and fail to generalize to novel, held-out scenarios. These works tend to rely heavily on pre-defined task schemas and limited environments. Despite impressive success rates on training environments (held-in tasks), their performance drops significantly on unobserved environments (held-out tasks).

The fundamental challenge lies in the agent's tendency to memorize observation-action pairings from training data rather than developing an understanding robust enough to generalize across diverse settings. Prior interventions, involving mixed training with general data, show some promise but aren't sufficient to handle various perturbations in task environments effectively.

Methodology: AgentRefine

AgentRefine introduces a self-refinement paradigm where models are trained to learn from their mistakes by interacting with a dynamically synthesized environment. This involves a three-step methodology:

1. Agent Synthesis Framework: The framework constructs a wide spectrum of environments and tasks rooted in diverse human personas, ensuring the agent encounters varied conditions that prevent overfitting to specific scenarios.
2. Interactive Trajectory Simulation: During multi-turn interactions, an agent receives feedback after each action execution. Errors in action steps—whether logical, formatting, or parameter-related—are specifically flagged, prompting the agent to refine its strategy based on the feedback.
3. Refinement Tuning on Self-Refined Data: Utilizing data that include refinements made during the agent's evolution through incorrect states boosts learning. This enrichment of the training corpus contributes to the model's capacity to adapt and succeed in previously unseen environments.

Key Experimental Insights

Extensive evaluations reveal that AgentRefine surpasses state-of-the-art approaches in terms of generalization. Notably, it far exceeds in tasks involving significant environmental changes or perturbations. The robustness demonstrated by AgentRefine, as opposed to memorization-focused models, underscores its efficiency in avoiding repetition of mistakes and seeking alternative pathways toward task completion.

Further analysis reveals that significant performance improvements are linked to three primary features:

• The model's ability to self-correct based on real-time feedback;
• The high diversity of training environments and tasks;
• A richer spectrum of thought processes enabled by synthesizing varied problems.

Implications and Future Directions

AgentRefine sets forth a new paradigm in training LLM-based agents, emphasizing adaptive learning through error feedback mechanisms. The broader implication is the potential shift towards generalized agents capable of functioning across a multitude of uncharted domains with high adaptability. This yields promising avenues for deploying autonomous systems in dynamic and multifaceted real-world contexts without requiring exhaustive manual fine-tuning for each new environment.

Future research might explore deeper integration with reinforcement learning techniques to further refine the decision-making loop and self-refinement processes, possibly enabling even more sophisticated exploration and adaptation abilities in LLM agents. Additionally, expanding this framework's application across diverse AI challenges presents a substantial opportunity to strengthen the generalization of AI models at large.