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This work provides a new method for fine-tuning models with sparse attention that works for any KV cache policy, runs on a moderate hardware budget, and allows the model to co-adapt with the policy, often outperforming models trained with exact attention (sequence parallelism).
This work analyzes query logs from MotherDuck, a cloud analytics platform built on DuckDB, to show that real-world SQL extends far beyond analytical queries, with users frequently employing dialect-specific functionality that LLMs struggle with.
The factors underlying performance differences across matcher architectures are clarified and motivate future research and benchmark designs that better disentangle architectural choices from model-level factors while explicitly evaluating distribution shift and cross-dataset transferability.
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