DeIRD: A Decoupled Continuous Implicit Reasoning Framework for Dialogue Models

By leveraging the ”think before speaking” response paradigm, large models can generate a Chain-of-Thought (CoT) prior to answering to enhance their deep reasoning capabilities. However, directly applying this paradigm to spoken dialogues poses three severe challenges: huge training costs caused by the modality gap, the unacceptable latency introduced by the ”think before speaking” mechanism, and limited reasoning exploration depth. To address these issues, we propose DeIRD, a latency-controllable and low-training-cost implicit reasoning framework for spoken dialogue models that simultaneously achieves ”think and speak decoupled” and ”think before speaking”. This framework is primarily realized through three core components: 1) We train a text VAE to compress variable-length textual CoT sequences into fixed-length continuous latents, serving as the carrier for implicit reasoning; 2) We introduce a continuous masked prediction model DMIR that takes user speech as input and outputs a fixed-length sequence of these latent targets via iterative confidence-guided ”mask-predict” decoding, to achieve ”think and speak decoupled” and ”think before speaking”; 3) We construct an implicit reasoning training dataset, LatentReason-40k, and find that the downstream backbones just require one epoch of end-to-end fine-tuning to acquire superior reasoning capabilities compared to explicit reasoning models trained on the same volume of data. Compared with various explicit-CoT baselines, DeIRD notably reduces the Time to First Audio (TTFA) while improving general dialogue quality, style control, deep reasoning, and reasoning diversity.