arXiv:2506.10341v2 Announce Type: replace Abstract: Interactively learning from observation and language feedback is an increasingly studied area driven by the emergence of large language model (LLM) agents. Despite impressive empirical demonstrations, so far a principled framing of these decision problems remains lacking. We formalize the Learning from Language Feedback (LLF) problem, assert sufficient assumptions to enable learning despite latent rewards, and introduce $\textit{transfer eluder dimension}$ as a measure to characterize the hardness of LLF. We formalize the intuition that infor
The proliferation of large language model (LLM) agents has created a pressing need for principled frameworks to guide their interactive learning, making this formalization timely.
Establishing a foundational theory for learning from language feedback is crucial for developing robust, predictable, and trustworthy AI agents, moving beyond empirical demonstrations.
The explicit formalization of the Learning from Language Feedback (LLF) problem and the introduction of 'transfer eluder dimension' provide new theoretical tools and metrics to measure and enhance AI agent capabilities.
- · AI researchers
- · LLM developers
- · Robotics
- · Autonomous systems
- · Empirical-only AI development
- · Unprincipled AI agent design
Improved performance and reliability of AI agents that learn from human-like instructions.
Accelerated development of general-purpose AI agents capable of complex interactive tasks.
Enhanced human-AI collaboration in critical applications due to more predictable and explainable agent behavior.
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Read at arXiv cs.LG