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arXiv · 2602.20528

Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning

Justin Lovelace·Christian Belardi·Sofian Zalouk·Adhitya Polavaram·Srivatsa Kundurthy·Kilian Q. Weinberger
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Abstract

The Stop-Think-AutoRegress Language Diffusion Model (STAR-LDM) integrates latent diffusion planning with autoregressive generation. Unlike conventional autoregressive language models limited to token-by-token decisions, STAR-LDM incorporates a "thinking" phase that pauses generation to refine a semantic plan through diffusion before continuing. This enables global planning in continuous space prior to committing to discrete tokens. Evaluations show STAR-LDM significantly outperforms similar-sized models on language understanding benchmarks and achieves $>70\%$ win rates in LLM-as-judge comparisons for narrative coherence and commonsense reasoning. The architecture also allows straightforward control through lightweight classifiers, enabling fine-grained steering of attributes without model retraining while maintaining better fluency-control trade-offs than specialized approaches.

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Justin Lovelace, Christian Belardi, Sofian Zalouk, Adhitya Polavaram, Srivatsa Kundurthy, Kilian Q. Weinberger. 2026-02-24. Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning. https://arxiv.org/abs/2602.20528

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