ThinkRouter: Efficient Reasoning via Routing Thinking between Latent and Discrete Spaces
Recent work explores latent reasoning to improve reasoning efficiency by replacing explicit reasoning trajectories with continuous representations in a latent space, yet its effectiveness varies across settings. Analysis of model confidence dynamics under latent reasoning reveals that thinking trajectories ending in incorrect answers contain fewer low-confidence steps than those ending in correct answers. Meanwhile, we suggest that soft embeddings aggregated by multiple low-confidence thinking alternatives may introduce and propagate noise, leading to high confidence in unreliable reasoning trajectories. Motivated by these observations, ThinkRouter, an inference-time confidence-aware routing mechanism is proposed to avoid high confidence and noise for efficient reasoning. ThinkRouter routes thinking to the discrete token space when model confidence is low, and to the latent space otherwise. Extensive experiments on STEM reasoning and coding benchmarks across diverse large reasoning models demonstrate that ThinkRouter outperforms explicit CoT, random routing, and latent reasoning baselines in terms of accuracy, achieving an average improvement of 19.70 points in Pass@1, while reducing generation length by up to 15.55%. Further comprehensive analysis reveals that ThinkRouter can calibrate errors arising from explicit CoT and latent reasoning, and accelerates end-of-thinking token generation by globally lowering model confidence.
698e905bcace060ff123ac79
Xin Xu
claimed_verified
2026-02-13T09:36:34.808Z
6190ab805ca89a28e9f66873
/avatars/3c7ecc398fbf851acd2a132e947a92be.svg
Xin Xu
XinXuNLPer
user
XinXuNLPer
698e905bcace060ff123ac7a
Tong Yu
698e905bcace060ff123ac7b
Xiang Chen
698e905bcace060ff123ac7c
Haoliang Wang
698e905bcace060ff123ac7d
Julian McAuley
698e905bcace060ff123ac7e
Saayan Mitra
ThinkRouter: Efficient Reasoning via Routing Thinking between Latent and Discrete Spaces
Recent work explores latent reasoning to improve reasoning efficiency by replacing explicit reasoning trajectories with continuous representations in a latent space, yet its effectiveness varies across settings. Analysis of model confidence dynamics under latent reasoning reveals that thinking trajectories ending in incorrect answers contain fewer low-confidence steps than those ending in correct answers. Meanwhile, we suggest that soft embeddings aggregated by multiple low-confidence thinking alternatives may introduce and propagate noise, leading to high confidence in unreliable reasoning trajectories. Motivated by these observations, ThinkRouter, an inference-time confidence-aware routing mechanism is proposed to avoid high confidence and noise for efficient reasoning. ThinkRouter routes thinking to the discrete token space when model confidence is low, and to the latent space otherwise. Extensive experiments on STEM reasoning and coding benchmarks across diverse large reasoning models demonstrate that ThinkRouter outperforms explicit CoT, random routing, and latent reasoning baselines in terms of accuracy, achieving an average improvement of 19.70 points in Pass@1, while reducing generation length by up to 15.55%. Further comprehensive analysis reveals that ThinkRouter can calibrate errors arising from explicit CoT and latent reasoning, and accelerates end-of-thinking token generation by globally lowering model confidence.
ThinkRouter is a confidence-aware routing mechanism that improves reasoning efficiency by switching between discrete token and latent spaces based on model confidence, achieving better accuracy and faster generation.
latent reasoning continuous representations latent space discrete token space model confidence thinking trajectories soft embeddings reasoning efficiency inference-time routing ThinkRouter Pass@1 explicit CoT latent reasoning baselines