Learning When to Think While Listening in Large Audio-Language Models

Recent advances in Large Audio-Language Models (LALMs) have made real-time, streaming spoken interaction increasingly practical. In this setting, reasoning quality and responsiveness are tightly coupled: reasoning that starts after the speech endpoint remains on the response path, while reasoning under partial audio risks missing decisive late evidence. We introduce a learnable wait-think-answer control formulation for LALMs. Motivated by the incremental nature of human conversation, the controller decides under partial audio evidence when to wait, and when to produce a compact reasoning update. Using Qwen2.5-Omni-7B as the base model, we construct aligned wait-think-answer traces from spoken reasoning data, train the controller with supervised fine-tuning (SFT), and then apply Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO). The reward combines answer correctness, action validity, update timing, post-end residual work, reasoning quality, and chain consistency, optimizing the complete wait-think-answer trajectory and not the final answer alone. On a six-task synthetic spoken reasoning question answering (SRQA) benchmark, the six-reward DAPO controller improves the row-weighted accuracy from 67.6% to 70.3% while reducing post-endpoint residual reasoning from 10.44 to 8.99 tokens under the same Qwen streaming replay protocol. These tokens measure remaining explicit reasoning, not elapsed response time. Native-direct accuracy is essentially unchanged after controller post-training. On a 186-item human-recorded Real Audio Bench, a transfer check beyond text-to-speech (TTS)-rendered speech, the controller family remains functional: SFT achieves the strongest accuracy, while the six-reward DAPO controller is the only learned variant whose post-end residual falls below the base. These results show that, in the endpoint-gated Qwen setting, a streaming model can learn when to make intermediate reasoning explicit during the audio stream.