ASU 魏华：大模型智能体正在重蹈强化学习的覆辙，如何跨越「仿真到现实」的鸿沟？ | IJCAI 2026
Ranking
Observed public metrics from 1 member.
Merged summary
TL;DR - ASU researcher Hua Wei argues that foundation-model agents face the same sim-to-real gaps long studied in reinforcement learning, including shifts in observations, actions, dynamics, and rewards. Adapting domain randomization and uncertainty quantification could make agents more robust across languages and real-world environments while limiting human supervision.
- Treating languages and deployment settings as distinct environments provides a unified MDP perspective on agent reliability and cross-environment generalization.
- Randomizing prompts, text, action spaces, and reward spaces reportedly enabled a 3B model to outperform a 32B model on cross-environment tasks.
- Reliable deployment requires agents to quantify uncertainty, identify fragile or erroneous reasoning steps, and escalate ambiguous decisions to humans.
- Shared terminology and evaluation standards could transfer mature sim-to-real methods from robotics and reinforcement learning to foundation-model agents.
Sources (1)
ASU 魏华：大模型智能体正在重蹈强化学习的覆辙，如何跨越「仿真到现实」的鸿沟？ | IJCAI 2026
TL;DR - ASU researcher Hua Wei argues that foundation-model agents face the same sim-to-real gaps long studied in reinforcement learning, including shifts in observations, actions, dynamics, and rewards. Adapting domain randomization and uncertainty quantification could make agents more robust across languages and real-world environments while limiting human supervision.
- Treating languages and deployment settings as distinct environments provides a unified MDP perspective on agent reliability and cross-environment generalization.
- Randomizing prompts, text, action spaces, and reward spaces reportedly enabled a 3B model to outperform a 32B model on cross-environment tasks.
- Reliable deployment requires agents to quantify uncertainty, identify fragile or erroneous reasoning steps, and escalate ambiguous decisions to humans.
- Shared terminology and evaluation standards could transfer mature sim-to-real methods from robotics and reinforcement learning to foundation-model agents.