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SWEAR introduces a novel framework for large audio-language models that optimizes reasoning efficiency by dynamically allocating computational effort based on five-dimensional difficulty scores. By leveraging reinforcement learning to compress traces for easier tasks while maintaining high accuracy on complex queries, the method significantly reduces token generation without compromising performance on hard instances. This work directly addresses the trade-off between speed and precision in adaptive reasoning systems for multimodal AI.
SWEAR: Section-Wise Efficient Audio Reasoning via Self-Rated Difficulty
arXiv:2610.13081v1 Announce Type: new Abstract: Recent work on large audio-language models (LALMs) explores adaptive reasoning to balance accuracy and efficiency. However, existing approaches often rely on coarse-grained difficulty estimation, overlooking the diverse factors that determine reasoning effort. We address this limitation by deriving five-dimensional difficulty scores and compressed reasoning traces from complementary offline assessments. Based on this characterization, we propose SWEAR, a difficulty-aware reasoning framework that learns difficulty signals during supervised fine-tuning and uses reinforcement learning to adapt reasoning allocation across reasoning stages. Experiments on MMAU-test-mini and MMAR show that SWEAR improves accuracy while substantially reducing generated tokens, shortening reasoning for easy instances without sacrificing performance on difficult ones.
Abstract:Recent work on large audio-language models (LALMs) explores adaptive reasoning to balance accuracy and efficiency. However, existing approaches often rely on coarse-grained difficulty estimation, overlooking the diverse factors that determine reasoning effort. We address this limitation by deriving five-dimensional difficulty scores and compressed reasoning traces from complementary offline assessments. Based on this characterization, we propose SWEAR, a difficulty-aware reasoning framework that learns difficulty signals during supervised fine-tuning and uses reinforcement learning to adapt reasoning allocation across reasoning stages. Experiments on MMAU-test-mini and MMAR show that SWEAR improves accuracy while substantially reducing generated tokens, shortening reasoning for easy instances without sacrificing performance on difficult ones.
Artificial Intelligence (cs.AI)
arXiv:2610.13081 [cs.AI]
(or arXiv:2610.13081v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.13081 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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