ELF-REG: Scaling Continuous Diffusion Language Models to Reasoning Tasks
Fully continuous diffusion language models (dLMs) denoise continuous representations without intermediate discretization, then decode all response tokens in parallel at the final step. Their performance on challenging reasoning tasks remains less established than that of autoregressive (AR) LLMs and masked dLMs. We scale Embedded Language Flows (ELF) to mathematical reasoning and code generation on GSM8K, MATH-500, HumanEval, and MBPP. We introduce ELF-REG, which improves learning with representation alignment and entanglement (REPA+REG), where a frozen AR teacher supervises intermediate denoiser features and supplies a global representation that is jointly denoised with the response. ELF...
| Authors | Zeyu Michael Li, William Xingxu Chen, Bingshuo Qian et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 September 2026 |
| Last updated at source | 8 October 2026 |
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