思考-言语：一种受控交错推理方法用于实时语音生成
Thinking-while-speaking: A Controlled, Interleaved Reasoning Method for Real-Time Speech Generation
AI总结：
本文提出了一种受控交错推理方法InterRS，用于实时语音生成，通过在自然语音生成过程中插入推理步骤，提高了语音流畅性和推理深度，实验表明其在数学和逻辑基准测试中表现更优，并生成更自然流畅的答案。
摘要：
思考-言语范式旨在使AI交流更人性化。关键挑战是保持流畅的语音生成同时进行深度推理。我们的方法InterRS通过在自然语音生成过程中插入推理步骤来解决这一问题。这需要高质量的数据，其中推理和语音精确对齐且长度比例受控。我们引入了一种新的管道来生成无缝交错的音频数据。为了训练我们的模型，我们结合了交错SFT与精炼数据以及强化学习，使用两种新的奖励：TA-Balance Reward用于管理时间与思考-回答比例，以及Linguistic Quality Reward用于优化表达。实验表明，我们的方法在数学和逻辑基准测试中实现了13%的性能提升，同时生成像口语指令模型一样快速的响应。此外，我们的方法生成的答案例句比先前方法更自然流畅。
英文摘要：
The thinking-while-speaking paradigm aims to make AI communication more human. A key challenge is maintaining fluent speech while performing deep reasoning. Our method, InterRS, tackles this by inserting reasoning steps only during natural speech generation. This requires high-quality data where reasoning and speech are precisely aligned, and the length ratio are under controlled. We introduce a novel pipeline to generate such seamlessly interleaved audio data. To train our model, we combine interleaved SFT with refined data and reinforcement learning with two new rewards: a TA-Balance Reward to manage timing and thinking-answer ratio, and a Linguistic Quality Reward to refine expression. Experiments show our approach achieves 13% better performance on mathmatical and logic benchmarks while generating instant response like a spoken-language instruct model which outputs fast CoT response. Furthermore, our method generates more natural and fluent answers than prior methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
