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Source note3 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 to test how well AI agents can generalize their skills from static benchmarks to real-world scenarios with dynamic user queries and tools. The study finds that both Supervised Fine-Tuning (SFT) and Reinforcement Learning trained agents struggle when faced with changes in the environment. To improve agent robustness, they propose Perturbation-Augmented Fine-Tuning.

What the Document Says

  • Large Language Model (LLM) agents perform well on static benchmarks but face difficulties in real-world scenarios due to dynamic user queries, tools, and interaction dynamics.
  • The researchers introduce OpenAgent, a problem setting for testing agent generalization across query, action, observation, and domain dimensions.
  • They construct a controlled environment with four tiers of environmental shifts: Perception, Interaction, Reasoning, and Internalization.
  • Results show that SFT and Reinforcement Learning trained agents experience varying degrees of performance degradation when dealing with open environmental shifts.
  • To enhance agent robustness, they propose Perturbation-Augmented Fine-Tuning as a disturbance-based intervention strategy for SFT.

Key Concepts Mentioned

  • Large Language Model (LLM) agents
  • Generalization gap
  • OpenAgent
  • Supervised Fine-Tuning (SFT)
  • Reinforcement Learning
  • Controlled environment
  • Environmental shifts
  • Perturbation-Augmented Fine-Tuning

Claims Made by the Document

  • LLM agents struggle in real-world scenarios due to dynamic elements.
  • OpenAgent can test agent generalization across multiple dimensions.
  • SFT and Reinforcement Learning trained agents underperform in open environments.
  • Perturbation-Augmented Fine-Tuning can enhance agent robustness.

Evidence Used

  • The text mentions the difficulties faced by LLM agents in real-world scenarios and the construction of OpenAgent to address this issue.
  • The study conducts experiments in a controlled environment to diagnose the impact of open environmental shifts on agents.

Methods / Approach

  • Formalizing OpenAgent, constructing a controlled environment, conducting experiments

Limitations Stated by the Authors

The study focuses on testing agent generalization in specific dimensions and does not explore all aspects of real-world scenarios.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The text does not provide enough information to assess the agent's ability to handle complex, unpredictable situations outside the controlled environment.

Open Questions

  • How well do agents perform in uncontrolled, unpredictable real-world scenarios after applying Perturbation-Augmented Fine-Tuning?
  • Can OpenAgent be applied to other types of AI models beyond LLM agents?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The study focuses on Large Language Model (LLM) agents and does not mention other types of AI agents.
  • The text does not discuss the specifics of the controlled environment or how it simulates real-world scenarios.

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.