← Back to all news
arXiv AI By Zhuoxuan Zhang (Yang), Kangqi Ni (Yang), Yuhang Chen (Yang), Mingfu Liang (Yang), Xiaohan Wei (Yang), Yunchen Pu (Yang), Fei Tian (Yang), Chonglin Sun (Yang), Frank Shyu (Yang), Adam (Yang), Song, Sandeep Pandey, Luke Simon, Tianlong Chen, Xi Liu

Diffusion-GR2: Diffusion Generative Reasoning Re-ranker

Read the original on arXiv AI →

arXiv:2607. 01170v1 Announce Type: cross Abstract: Generative reasoning re-rankers achieve strong recommendation accuracy by emitting a chain-of-thought before re-ordering a candidate list, but they are slow at inference: an autoregressive (AR) decoder spends one sequential forward pass per reasoning token, and the reasoning trace far exceeds the ranking it produces.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Sep 2

Flow Reasoning Models: Turning Flows Into Efficient Recurrent Reasoners

Flow Reasoning Models (FRMs) are a new framework that turns continuous flow models into efficient recurrent reasoners for structured tasks. By self‑conditioning a flow model on its own past outputs, FRMs iteratively refine solutions, allowing parallel decision making and revision. The authors introduce Fixed‑Point Forcing (FPF) to mitigate exposure bias at deeper recursion, and report near‑perfect solve rates on Sudoku‑Extreme, Zebra, and Maze‑Unique, outperforming existing masked‑diffusion and specialized baselines while using far fewer inference FLOPs.

By Alec Helbling, Andrey Bryutkin, Mauro Martino, Duen Horng Chau, Nima Dehmamy, Hendrik Strobelt
diffusionsafety
More like this →
arXiv Machine Learning
Jun 2

d2: Improving Reasoning in Diffusion Language Models via Trajectory Likelihood Estimation

arXiv:2509. 21474v4 Announce Type: replace Abstract: While diffusion language models (DLMs) have achieved competitive performance in text generation, improving their reasoning ability with reinforcement learning remains an active research area.

By Guanghan Wang, Gilad Turok, Yair Schiff, Marianne Arriola, Volodymyr Kuleshov
llmsdiffusionreinforcement-learningbenchmarks
More like this →
arXiv Machine Learning
Sep 25

ELF-REG: Scaling Continuous Diffusion Language Models to Reasoning Tasks

The paper introduces ELF-REG, a scaling of continuous diffusion language models (dLMs) for reasoning tasks. By aligning representations and using a frozen autoregressive teacher to supervise intermediate denoiser features, ELF-REG achieves higher pass@1 rates on GSM8K, MATH-500, HumanEval, and MBPP compared to prior dLMs. The method also enables strong low‑NFE performance through early‑stop decoding of intermediate predictions.

By Zeyu Michael Li, William Xingxu Chen, Bingshuo Qian, Jiayin Liu, Xiang Cheng
llmsdiffusionbenchmarkssafety
More like this →