Papers with in-context reinforcement learning
In-Context Reinforcement Learning with Retrieval-Augmented Generation for Text-to-SQL (2025.coling-main)
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| Challenge: | Existing methods of synthetic query generation generate mostly simple queries which might not be sufficiently representative of complex, real world queries. |
| Approach: | They propose to use large language models to fine tune query generation to produce complex queries that practitioners may pose during inference. |
| Outcome: | The proposed framework achieves 15-20% higher recall in database/table retrieval task compared to the existing state-of-the-art models for schema identification and upto 2% higher execution accuracy for SQL generation. |
A2O: LLM-based Agentic Learning of Action-to-Object Features for Video Action Recognition (2026.findings-acl)
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| Challenge: | Recent action recognition based on vision–language pretraining and self-supervised video foundation models tends to induce spurious correlations and shortcut learning by relying on action-irrelevant cues. |
| Approach: | They propose a framework in which an LLM agent integrates the two approaches within an agentic learning paradigm to design motion features tailored to the target actions. |
| Outcome: | The proposed model is based on the commonsense knowledge of large language models (LLMs) and the open vocabulary object detector to make the model attend to objects in a video required for recognizing the target actions. |