Diffusion language models (DLMs) have recently emerged as an alternative to
autoregressive approaches, offering parallel sequence generation and flexible
token orders. However, their inference remains slower than that of
autoregressive models, primarily due to the cost of bidirectional attention and
the large number of refinement steps required for high quality outputs. In this
work, we highlight and leverage an overlooked property of DLMs early answer
convergence: in many cases, the correct answer can be internally identified by
half steps before the final decoding step, both under semi-autoregressive and
random remasking schedules. For example, on GSM8K and MMLU, up to 97% and 99%
of instances, respectively, can be decoded correctly using only half of the
refinement steps. Building on this observation, we introduce Prophet, a
training-free fast decoding paradigm that enables early commit decoding.
Specifically, Prophet dynamically decides whether to continue refinement or to
go “all-in” (i.e., decode all remaining tokens in one step), using the
confidence gap between the top-2 prediction candidates as the criterion. It
integrates seamlessly into existing DLM implementations, incurs negligible
overhead, and requires no additional training. Empirical evaluations of
LLaDA-8B and Dream-7B across multiple tasks show that Prophet reduces the
number of decoding steps by up to 3.4x while preserving high generation
quality. These results recast DLM decoding as a problem of when to stop
sampling, and demonstrate that early decode convergence provides a simple yet
powerful mechanism for accelerating DLM inference, complementary to existing
speedup techniques. Our code is publicly available at
https://github.com/pixeli99/Prophet.