<ul data-eligibleForWebStory="true"><li>Interactively learning from observation and language feedback is a growing area of study due to large language model agents.</li><li>A new paper formalizes the Learning from Language Feedback (LLF) problem and introduces the transfer eluder dimension as a complexity measure.</li><li>Transfer eluder dimension indicates that feedback complexity affects learning in LLF problems.</li><li>The paper shows that learning from rich language feedback can be much faster than learning from reward.</li><li>An algorithm named HELiX is introduced to solve LLF problems with performance guarantees linked to transfer eluder dimension.</li><li>HELiX performs well in various domains, including instances where prompting LLMs repeatedly may not be reliable.</li><li>The contributions of the paper lay the groundwork for designing interactive learning algorithms from general language feedback.</li></ul>

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