SWEAR: Section-Wise Efficient Audio Reasoning via Self-Rated Difficulty

The paper introduces SWEAR, a framework for audio-language models that uses a five‑dimensional difficulty score to guide reasoning. By learning difficulty signals during fine‑tuning and applying reinforcement learning, SWEAR allocates reasoning effort adaptively across stages. Experiments on MMAU‑test‑mini and MMAR demonstrate that SWEAR boosts accuracy while cutting the number of generated tokens, especially for easier cases.

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• llms
• reinforcement-learning
• fine-tuning