Closing the Modality Reasoning Gap for Speech Large Language Models (2026.acl-long)
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| Challenge: | Recent advances in Speech Large Language Models have a modality reasoning gap that is not addressed by prior work. |
| Approach: | They propose a reinforcement-learning framework that aligns text-conditioned and speech-conditioned trajectories through an asymmetric reward design. |
| Outcome: | Experiments on MMSU and OBQA show that the proposed framework narrows the modality reasoning gap and achieves state-of-the-art performance among 7B-scale Speech LLMs. |
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