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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 for...
arXiv:2606. 14591v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have shown strong performance on a wide range of audio understanding tasks, yet they still struggle with complex audio reasoning.
arXiv:2603.02266v2 Announce Type: replace-cross Abstract: Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-La...
arXiv:2609.23589v1 Announce Type: cross Abstract: Large audio-language models (LALMs) are increasingly used for a broader range of audio reasoning tasks. These models typically incorporate audio repr...
The paper investigates how Audio Large Language Models (Audio LLMs) actually use audio input to determine answers, rather than relying on textual cues. It finds that replacing audio with silence or unrelated audio degrades performance more after training than before, that acoustic information shapes representations in early-to-middle layers and influences final predictions in middle-to-late layers, and that training impacts specific layer bands most strongly. These observations offer a mechanistic view of how training enhances the use of acoustic evidence in Audio LLMs.
Audio-R1 is a reinforcement learning post‑training framework that leverages audio self‑supervised learning tasks to provide verifiable rewards for large audio‑language models. By automatically determining answers during task construction, Audio‑R1 verifies model responses and computes rewards, leading to consistent performance gains across multiple models and audio benchmarks. Ablation studies show each self‑supervised task contributes to the improvement, and joint training yields additional benefits, especially on downstream tasks distinct from the training tasks.