Early Experience for Language Agent Learning
Автор: The Times of AI
Загружено: 2025-10-15
Просмотров: 62
Описание:
The sources primarily introduce and advocate for the Early Experience paradigm, a novel training approach for language agents designed to bridge the gap between traditional Imitation Learning (IL) and the aspirational Era of Experience (Reinforcement Learning). This paradigm addresses the limitations of relying solely on expert demonstrations or the challenges of environments lacking verifiable reward signals. Early Experience enables agents to learn and improve by generating and leveraging their own interaction data, using resulting future states as a scalable, reward-free source of supervision. The text details two specific strategies under this paradigm — Implicit World Modeling and Self-Reflection —demonstrating through extensive evaluation across eight diverse environments that these methods consistently enhance agent effectiveness, out-of-domain generalization, and provide a superior warm-start for subsequent reinforcement learning.
#ai #llm #reasoning
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