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 format changes. These findings suggest that post-training often teaches models to exploit task-specific textual cues and answer patterns rather than developing generalizable audio understanding and reasoning capabilities.
Figures & tables
Figure 1: Schematic overview of LALM post-training. The illustration demonstrates that models achieve comparable performance gains (e.g., ∼ +40) whether post-trained with informative audio (a) or silence (b).
MMAU
MMAR
Gold
Listed
Invalid
Gold
Listed
Invalid
Omni-3B
Base
63.1
29.4
7.5
51.8
45.0
3.2
SFT
69.8
28.6
1.6
55.3
43.5
1.2
RFT
65.4
29.0
5.6
52.7
44.1
3.2
GRPO
71.1
26.9
2.0
55.2
42.9
1.8
Table 5: Answer Distribution across Multiple-choice Options. ∗ Denotes open-source model.
Figure 2: Case Study: Revealing option priors and the impact of question phrasing on model predictions.
Appendix figures & tables2 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 3: The judge template; the three ⟨ slots ⟩ are filled per item. Blue counts as the same answer, purple does not.