Time conditioning often unnecessary in uniform discrete diffusion models
Does Uniform Discrete Diffusion Need Time?
Machine LearningArtificial IntelligenceComputation and Language
Summary
Uniform discrete diffusion models (UDMs) usually use time as an input to guide their predictions. The authors found that while the ideal predictor depends on time, in realistic language data scenarios, the model’s predictions hardly change with time. Experiments confirmed that ignoring the time input can work just as well or even better for language tasks. This suggests that explicitly adding time information might not be needed for many practical uses of UDMs.
What this means in practice
- •For language model developers: Simplify model design by removing explicit time inputs in diffusion-based language models without losing performance.
- •For natural language processing engineers: Reduce complexity and training resources when building language generation systems using discrete diffusion techniques.
Authors
Chunsan Hong, Chieh-Hsin Lai, Satoshi Hayakawa, Yuhta Takida, Jong Chul Ye, Yuki Mitsufuji
Abstract
Uniform discrete diffusion models (UDMs) commonly use explicit time conditioning, but we find that it can often be unnecessary in practice. In this paper, we first show that the population-optimal UDM predictor generally depends on time: time controls how much the model should trust the observed context. We then show that this dependence can become negligible in finite-data settings relevant to language. When a corrupted training sequence remains much closer to its original clean sequence than to competing training sequences, the empirical-optimal predictor is nearly insensitive to time over most of the diffusion trajectory, where the guarantee weakens toward the high-noise endpoint. Empirically, trained language UDMs exhibit limited time sensitivity over most of the trajectory, while time-agnostic predictors remain competitive with, and often outperform, time-conditioned models across datasets and training objectives. These results challenge the use of explicit time conditioning in UDMs: although the population optimum depends on time, explicitly conditioning on it may often be unnecessary in practice.