T$^\star$: Progressive Block Scaling for Masked Diffusion Language Models Through Trajectory Aware Reinforcement Learning

Hanchen Xia; Baoyou Chen; Yutang Ge; Guojiang Zhao; Siyu Zhu

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arXiv · 2601.11214

T$^\star$: Progressive Block Scaling for Masked Diffusion Language Models Through Trajectory Aware Reinforcement Learning

Hanchen Xia·Baoyou Chen·Yutang Ge·Guojiang Zhao·Siyu Zhu

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Abstract

We present T$^\star$, a simple TraceRL-based training curriculum for progressive block-size scaling in masked diffusion language models (MDMs). Starting from an AR-initialized small-block MDM, T$^\star$ transitions smoothly to larger blocks, enabling higher-parallelism decoding with minimal performance degradation on math reasoning benchmarks. Moreover, further analysis suggests that T$^\star$ may actually converge to an alternative decoding schedule that achieves comparable performance.

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Hanchen Xia, Baoyou Chen, Yutang Ge, Guojiang Zhao, Siyu Zhu. 2026-01-16. T$^\star$: Progressive Block Scaling for Masked Diffusion Language Models Through Trajectory Aware Reinforcement Learning. https://arxiv.org/abs/2601.11214

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