What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities
Post-training often improves the performance of large audio-language models on audio understanding and reasoning benchmarks. However, these improvements may reflect adaptation to specific question formats, such as multiple-choice questions, rather than genuine advances in audio understanding and reasoning capabilities. To investigate what LALMs actually learn from post-training, we conduct controlled experiments that disentangle improvements in audio understanding from adaptation to specific question formats. Across different models and post-training methods, we find that much of the reported improvement persists even without informative audio, but largely disappears when the question for...
| Authors | Hua-Hsuan Chen, Xuanjun Chen, Wei-Chung Lu et al. |
|---|---|
| Primary category | cs.SD |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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