aiqbench analysis
This study reveals that diffusion objectives fundamentally reshape internal representations to create global redundancy and reduce recency bias compared to autoregressive models. By leveraging this inherent depth redundancy in native dLLMs like LLaDA, researchers can achieve significant FLOPs reductions without collapsing performance on complex reasoning tasks. The findings suggest that training objectives are a more critical driver of model efficiency than architectural changes alone for future compression strategies.
A Comparative analysis of Layer-wise Representational Capacity in AR and Diffusion LLMs
arXiv:2603.07475v4 Announce Type: replace Abstract: Autoregressive (AR) language models build representations incrementally via left-to-right prediction, while diffusion language models (dLLMs) are trained through full-sequence denoising. Although recent dLLMs match AR performance, whether diffusion objectives fundamentally reshape internal representations remains unclear. We perform the first layer- and token-wise representational analysis comparing native dLLMs (LLaDA), native AR models (Qwen2.5), and AR-initialized dLLMs (Dream-7B), using cosine similarity across layers and tokens alongside static inference-time layer-skipping as an analytical probe of redundancy. We find that diffusion objectives produce more global representations with substantial early-layer redundancy and reduced recency bias, while AR objectives yield tightly coupled, locally structured representations. AR-initialized dLLMs retain AR-like dynamics despite diffusion training, revealing persistent initialization bias. Leveraging this redundancy, native dLLMs absorb up to 18.75% FLOPs reduction while retaining over 90% performance on math-reasoning and coding benchmarks, whereas AR models collapse under identical skipping, revealing that diffusion objectives, rather than architecture alone, induce depth redundancy that enables principled compression.
Abstract:Autoregressive (AR) language models build representations incrementally via left-to-right prediction, while diffusion language models (dLLMs) are trained through full-sequence denoising. Although recent dLLMs match AR performance, whether diffusion objectives fundamentally reshape internal representations remains unclear. We perform the first layer- and token-wise representational analysis comparing native dLLMs (LLaDA), native AR models (Qwen2.5), and AR-initialized dLLMs (Dream-7B), using cosine similarity across layers and tokens alongside static inference-time layer-skipping as an analytical probe of redundancy. We find that diffusion objectives produce more global representations with substantial early-layer redundancy and reduced recency bias, while AR objectives yield tightly coupled, locally structured representations. AR-initialized dLLMs retain AR-like dynamics despite diffusion training, revealing persistent initialization bias. Leveraging this redundancy, native dLLMs absorb up to 18.75% FLOPs reduction while retaining over 90% performance on math-reasoning and coding benchmarks, whereas AR models collapse under identical skipping, revealing that diffusion objectives, rather than architecture alone, induce depth redundancy that enables principled compression.
v4: improving writing and adding Qwen2.5-Instruct results with all v3 changes
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2603.07475 [cs.CL]
(or arXiv:2603.07475v4 [cs.CL] for this version)
From: Raghavv Goel Mr. [view email] [v1] Sun, 8 Mar 2026 05:31:52 UTC (43,192 KB) [v2] Mon, 27 Apr 2026 07:54:04 UTC (131,896 KB) [v3] Fri, 8 May 2026 03:23:47 UTC (2,819 KB) [v4] Sun, 2 Aug 2026 20:40:52 UTC (7,187 KB)
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