The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective
Quick Answer
This paper addresses the sim-to-real gap for foundation model agents by framing it within a Markov Decision Process (MDP) structure.
Quick Take
It advocates for established solutions like domain randomization to enhance agent robustness, aiming to create standardized benchmarks for reliable real-world applications.
Key Points
- Proposes a unified MDP perspective to tackle the sim-to-real gap in foundation models.
- Highlights the importance of domain randomization for improving agent robustness.
- Aims to establish standardized benchmarks for evaluating foundation model agents.
- Demonstrates severe observation space gaps leading to operational failures.
- Sets a comprehensive research agenda to bridge classical discrepancies in AI.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 07017v1 Announce Type: new Abstract: Foundation model agents are increasingly deployed for real-world decision-making, but suffer from the sim-to-real gap. While robotics and classical control have mature frameworks to address this gap, the foundation model community is treating agent robustness as an entirely novel phenomenon.
Our paper proposes formalizing the foundation model and training gap as a classical sim-to-real problem structured entirely around the four elements of a Markov Decision Process, including Observation, Action, Transition, and Reward. In this paper, we set a comprehensive research agenda that translates classical discrepancies into the foundation model domain and advocates for adopting established solutions like domain randomization.
We provide concrete examples, such as a multilingual to demonstrate how severe observation space gaps lead to operationally invalid actions despite correct semantic intent. Ultimately, this agenda aims to drive a paradigm shift, yielding a unified vocabulary and standardized stress test benchmarks to foster a new generation of highly trustworthy agents for reliable real-world applications.
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