Paper
Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning
Authors
Justin Lovelace, Christian Belardi, Sofian Zalouk, Adhitya Polavaram, Srivatsa Kundurthy, Kilian Q. Weinberger
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.20528v1</id>\n <title>Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning</title>\n <updated>2026-02-24T04:09:31Z</updated>\n <link href='https://arxiv.org/abs/2602.20528v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.20528v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CL'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <published>2026-02-24T04:09:31Z</published>\n <arxiv:comment>COLM 2025</arxiv:comment>\n <arxiv:primary_category term='cs.CL'/>\n <author>\n <name>Justin Lovelace</name>\n </author>\n <author>\n <name>Christian Belardi</name>\n </author>\n <author>\n <name>Sofian Zalouk</name>\n </author>\n <author>\n <name>Adhitya Polavaram</name>\n </author>\n <author>\n <name>Srivatsa Kundurthy</name>\n </author>\n <author>\n <name>Kilian Q. Weinberger</name>\n </author>\n </entry>"
}