SWEAR: Section-Wise Efficient Audio Reasoning via Self-Rated Difficulty
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.
ProofPaper ↗
Key points
- 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.
- 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.
Sources (1)
- [1]SWEAR: Section-Wise Efficient Audio Reasoning via Self-Rated DifficultyarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 06:36 AM
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.
Recent work on large audio-language models (LALMs) explores adaptive reasoning to balance accuracy and efficiency.
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