• Computer Science > Artificial Intelligence [Submitted on 17 Feb 2026] Title:Improving Interactive In-Context Learning from Natural Language Feedback View PDF HTML (experimental)Abstract:Adapting one’s thought process based on corrective feedback is an essential ability in human learning, particularly in collaborative settings. • In contrast, the current large language model training paradigm relies heavily on modeling vast, static corpora. • While effective for knowledge acquisition, it overlooks the interactive feedback loops essential for models to adapt dynamically to their context. • In this work, we propose a framework that treats this interactive in-context learning ability not as an emergent property, but as a distinct, trainable skill. • We introduce a scalable method that transforms single-turn verifiable tasks into multi-turn didactic interactions driven by information asymmetry. • We first show that current flagship models struggle to integrate corrective feedback on hard reasoning tasks.
Article Summaries:
- Computer Science > Artificial Intelligence [Submitted on 17 Feb 2026] Title:Improving Interactive In-Context Learning from Natural Language Feedback View PDF HTML (experimental)Abstract:Adapting one’s thought process based on corrective feedback is an essential ability in human learning, particularly in collaborative settings. In contrast, the current large language model training paradigm relies heavily on modeling vast, static corpora. While effective for knowledge acquisition, it overlooks the interactive feedback loops essential for models to adapt dynamically to their context. In this wor
Sources: