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Source note2 min read

Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use

Source

arXiv

arxiv.org/abs/2607.01084v1

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Source

Plain-Language Brief

Researchers introduce OpenAgent, a problem setting designed to help agents generalize better in real-world scenarios with dynamic queries, tools, and interactions. The study reveals that both Supervised Fine-Tuning and Reinforcement Learning methods suffer from performance degradation when confronted with open environmental shifts. A new approach called Perturbation-Augmented Fine-Tuning is proposed to enhance agent robustness in realistic environments.

What the Document Says

  • The paper discusses the challenges faced by Large Language Model (LLM) agents in real-world scenarios due to dynamic user queries, tool sets, and interaction dynamics.
  • The authors introduce OpenAgent, a problem setting for Tool-Use Agents in an open world with distributional shifts across query, action, observation, and domain dimensions.
  • A controlled sandbox environment is created for systematic diagnosis of the impact of these environmental shifts.
  • Four tiers of hierarchy are defined: Perception, Interaction, Reasoning, and Internalization.
  • Experiments reveal that agents trained via Supervised Fine-Tuning (SFT) and Reinforcement Learning suffer from varying degrees of performance degradation when facing open environmental shifts.
  • A new method called Perturbation-Augmented Fine-Tuning is proposed to improve agent robustness and utility in realistic environments.

Key Concepts Mentioned

  • Large Language Model (LLM) agents
  • OpenAgent
  • Supervised Fine-Tuning (SFT)
  • Reinforcement Learning
  • controlled sandbox environment
  • four-tier hierarchy
  • Perturbation-Augmented Fine-Tuning

Claims Made by the Document

  • Agents struggle in real-world scenarios due to dynamic user queries, tool sets, and interactions.
  • OpenAgent problem setting helps agents generalize better in real-world scenarios.
  • SFT and Reinforcement Learning methods suffer from performance degradation in open environmental shifts.

Evidence Used

  • The paper discusses the challenges faced by LLM agents in real-world scenarios due to dynamic user queries, tool sets, and interaction dynamics.
  • The authors introduce OpenAgent, a problem setting for Tool-Use Agents in an open world with distributional shifts across query, action, observation, and domain dimensions.
  • Experiments reveal that agents trained via SFT and Reinforcement Learning suffer from varying degrees of performance degradation when facing open environmental shifts.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

While Large Language Model (LLM) agents demonstrate proficiency in static benchmarks, their deployment in real-world scenarios is hindered by the dynamic nature of user queries, tool sets, and interaction dynamics. To address this generalization gap, we formalize OpenAgent (Tool-Use Agent in Open-World), a problem setting characterized by distributional shifts across query, action, observation, and domain dimensions. To systematically diagnose its impact, we construct a controlled sandbox environment where we define fine-grained environmental shifts across a four-tier hierarchy, Perception, Interaction, Reasoning, and Internalization, and conduct a comprehensive series of experiments. Our analysis yields a series of key insights, demonstrating that agents trained via both Supervised Fine-Tuning(SFT) and Reinforcement Learning suffer from varying degrees of performance degradation when confronting open environmental shifts. Building on these insights, we propose Perturbation-Augmented Fine-Tuning, a disturbance-based intervention strategy for SFT that lays the foundation for enhancing agent robustness and utility in realistic environments. Our code will be released at: https://github. com/LAMDA-NeSy/OpenAgent.