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[2602.16066] Improving Interactive In-Context Learning from Natural Language Feedback
Summary
This paper presents a novel framework for improving interactive in-context learning in large language models by utilizing natural language feedback, enhancing their adaptability and performance across various tasks.
Why It Matters
As AI models increasingly rely on static data, this research highlights the importance of dynamic feedback mechanisms in training, which could lead to more effective and adaptable AI systems. The findings suggest a significant step toward enabling models to learn interactively, enhancing their utility in real-world applications.
Key Takeaways
- Proposes a framework for interactive in-context learning based on feedback.
- Demonstrates improved performance of smaller models using this method.
- Shows potential for out-of-distribution generalization across diverse tasks.
- Highlights the importance of modeling feedback environments for self-improvement.
- Suggests that interactive learning can enhance AI adaptability.
Computer Science > Artificial Intelligence arXiv:2602.16066 (cs) [Submitted on 17 Feb 2026] Title:Improving Interactive In-Context Learning from Natural Language Feedback Authors:Martin Klissarov, Jonathan Cook, Diego Antognini, Hao Sun, Jingling Li, Natasha Jaques, Claudiu Musat, Edward Grefenstette View a PDF of the paper titled Improving Interactive In-Context Learning from Natural Language Feedback, by Martin Klissarov and 6 other authors 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. We then demonstrate that models trained with our approach dramatically improve the ability to interactively learn from language feedback. More specifically, the multi-turn performance of a smal...
