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[2510.04373] JEF-Hinter: Leveraging Offline Knowledge for Improving Web Agents Adaptation
Summary
The paper presents JEF-Hinter, a system designed to enhance the adaptation of web agents by leveraging offline knowledge, improving performance in unfamiliar domains without costly online interactions.
Why It Matters
As large language models (LLMs) face challenges in adapting to new domains, JEF-Hinter offers a cost-effective solution by utilizing offline trajectories. This innovation is crucial for advancing AI capabilities while minimizing the risks associated with traditional adaptation methods.
Key Takeaways
- JEF-Hinter distills offline knowledge into compact hints for web agents.
- The system effectively utilizes both successful and failed trajectories for guidance.
- It supports parallel hint generation and is benchmark-independent.
- Experiments show JEF-Hinter outperforms existing methods, including human-based hints.
- The approach enhances transparency and traceability in agent decision-making.
Computer Science > Artificial Intelligence arXiv:2510.04373 (cs) [Submitted on 5 Oct 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:JEF-Hinter: Leveraging Offline Knowledge for Improving Web Agents Adaptation Authors:Hadi Nekoei, Aman Jaiswal, Patrice Bechard, Oleh Shliazhko, Orlando Marquez Ayala, Mathieu Reymond, Massimo Caccia, Alexandre Drouin, Sarath Chandar, Alexandre Lacoste View a PDF of the paper titled JEF-Hinter: Leveraging Offline Knowledge for Improving Web Agents Adaptation, by Hadi Nekoei and 9 other authors View PDF HTML (experimental) Abstract:Large language model (LLM) agents perform well in sequential decision-making tasks, but improving them on unfamiliar domains often requires costly online interactions or fine-tuning on large expert datasets. These strategies are impractical for closed-source models and expensive for open-source ones, with risks of catastrophic forgetting. Offline trajectories offer reusable knowledge, yet demonstration-based methods struggle because raw traces are long, noisy, and tied to specific tasks. We present Just-in-time Episodic Feedback Hinter (JEF-Hinter), an agentic system that distills offline traces into compact, context-aware hints. A zooming mechanism highlights decisive steps in long trajectories, capturing both strategies and pitfalls. Unlike prior methods, JEF-Hinter leverages both successful and failed trajectories, extracting guidance even when only failure data is available, while supporting parall...
