Researchers introduce Introspective Diffusion Language Models (I-DLM), using introspective strided decoding to enable parallel token generation while matching autoregressive model quality. An 8B-parameter I-DLM outperforms the larger 16B LLaDA-2.1-mini by +26 points on AIME-24 and achieves 2.9-4.1x throughput improvement, addressing the historical quality gap in diffusion language models. The method is integrated directly into SGLang serving infrastructure without custom changes.
Models
Introspective Diffusion Language Models
Introspective Diffusion Language Models enable parallel token generation with 2.9-4.1x speedup—an 8B model beats a 16B baseline by 26 points on AIME-24 without custom serving changes.
Tuesday, April 14, 2026 12:00 PM UTC2 MIN READSOURCE: Hacker NewsBY sys://pipeline
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models