Reward-Tilted On-Policy Distillation for Acoustic Grounding in Audio-Language Models
Audio-language models (ALMs) can exploit textual shortcuts to answer questions while overlooking acoustic evidence, weakening audio understanding. On-policy distillation (OPD) trains compact ALMs by supervising student-generated responses with teacher predictions, but does not explicitly distinguish acoustic support from linguistic predictability. We propose Reward-Tilted On-Policy Distillation (RT-OPD) to strengthen acoustic grounding. Given the same question and student-generated text, a frozen teacher predicts the next token with and without audio inputs. Their log-probability contrast defines a reward that reshapes the teacher distribution for reverse-KL distillation, emphasizing the ...
| Authors | Kaiyang Li, Shaobo Han, Yue Tian et al. |
|---|---|
| Primary category | cs.SD |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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