arXiv:2603.24596v3 Announce Type: replace-cross Abstract: While the shift from cascaded dialogue systems to end-to-end (E2E) speech Large Language Models (LLMs) improves latency and paralinguistic modeling, E2E models often exhibit a significant performance degradation compared to their text-based counterparts. The standard Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) training methods fail to close this gap. To address this, we propose X-OPD, a novel Cross-Modal On-Policy Distillation framework designed to systematically align the capabilities of Speech LLMs to their text-based
The rapid advancement in large language models has exposed a performance gap in their speech counterparts, making methods to align capabilities a critical and timely research focus.
Improving the performance of end-to-end speech LLMs will democratize advanced AI functionalities, making them more accessible and effective across various applications.
The proposed X-OPD framework promises to significantly bridge the capability gap between text-based and speech-based LLMs, leading to more robust and versatile AI systems.
- · AI developers
- · Speech technology companies
- · Software as a Service (SaaS)
- · Legacy cascaded dialogue systems
- · Developers reliant solely on text-based LLMs for high-performance tasks
End-to-end speech LLMs will achieve performance closer to their text-based counterparts, making them more commercially viable for complex tasks.
Improved speech LLMs will accelerate the development of more natural and intuitive human-computer interfaces, expanding AI use cases beyond traditional text inputs.
The widespread adoption of highly capable speech AI could fundamentally alter workflows in customer service, accessibility technology, and multilingual communication.
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Read at arXiv cs.AI