Sheng-Yan Hao

Enhancing Auditory Reasoning in Large Audio–Language Models Via Supervised Fine-Tuning and Reinforcement Learning With Verifiable Rewards

The objective of this paper is to improve and analyze auditory reasoning in large audio–language models for audio question answering (AQA), where a model must infer the correct answer from acoustic evidence and textual answer options. Although reinforcement learning (RL) with verifiable rewards has recently improved re...