Text Generation Beyond Discrete Token Sampling
Authors: name: Yufan Zhuang, name: Liyuan Liu, name: Chandan Singh, name: Jingbo Shang, name: Jianfeng Gao

In standard autoregressive generation, an LLM predicts the next-token
distribution, samples a discrete token, and then discards the distribution,
passing only the sampled token as new input. To preserve this distribution's
rich information, we propose Mixture of Inputs (MoI), a training-free method
for autoregressive generation. After generating a token following the standard
paradigm, we construct a new input that blends the generated discrete token
with the previously discarded token distribution. Specifically, we employ a
Bayesian estimation method that treats the token distribution as the prior, the
sampled token as the observation, and replaces the conventional one-hot vector
with the continuous posterior expectation as the new model input. MoI allows
the model to maintain a richer internal representation throughout the
generation process, resulting in improved text quality and reasoning
capabilities. On mathematical reasoning, code generation, and PhD-level QA
tasks, MoI consistently improves performance across multiple models including
QwQ-32B, Nemotron-Super-49B, Gemma-3-27B, and DAPO-Qwen-32B, with no additional
training and negligible computational overhead.