When to Commit? Variable-Size Self-Contained Blocks for Discrete Diffusion Language Models

Discrete diffusion language models (dLLMs) enable parallel token updates with bidirectional attention, yet typical generation adopts blockwise semi-autoregressive decoding, where fixed-size or heuristic blocks are committed to the prefix. This creates a future-dependent commitment problem: tokens may be frozen before enough future context is available, causing early local errors to propagate across later blocks. Motivated by this, we propose Variable-Size Self-Contained Blocks (VSB), a commitment criterion that replaces fixed boundaries with an operational self-containedness test. VSB follows a propose-then-test design, where the bidirectional dLLM first proposes a window hypothesis, then compares the predictive distributions under two views of that hypothesis: a No-Future view truncated at a candidate boundary and a Future-Aware view over the full window. Low NF-FA divergence indicates that the candidate block remains consistent even when the hypothesised future is hidden, making it a self-contained block to commit. VSB applies to pretrained dLLMs and can be paired with optional No-Future training for stronger alignment. Across reasoning, code, and general knowledge benchmarks, VSB improves over fixed-size and heuristic block baselines.