Diffusion-GR2: Diffusion Generative Reasoning Re-ranker
Score
59Why selected
Strong methodological innovation combining diffusion models with reasoning re-ranking, with solid experiments, yet validated on only one dataset, leaving generalizability to be tested.
AI commentary
「This work provides a practical parallel solution to the inference bottleneck of generative reasoning re-rankers, significantly boosting throughput without sacrificing recommendation accuracy, enabling real-time deployment in large-scale systems.」
Quick overview
Diffusion-GR2 converts an autoregressive reasoning re-ranker into a diffusion model, achieving 2.4–3.5× decode throughput gain via parallel denoising, while closing accuracy gaps through conversion fine-tuning, on-policy distillation, and reinforcement learning, reaching near-parity on Amazon Beauty.
- Proposes Diffusion-GR2, converting an autoregressive reasoning re-ranker into a block-diffusion language model for parallel decoding speedup.
- Conversion fine-tuning (CFT) closes the structural gap to ensure valid permutations (no duplicates, drops, or out-of-set IDs).
- On-policy distillation (OPD) and reinforcement learning further close the distributional gap to restore accuracy.
- On Amazon Beauty, achieves 2.4–3.5× throughput gain while maintaining near-parity accuracy with the AR baseline.
Full text
Computer Science > Information Retrieval
arXiv:2607.01170v1 (cs) [Submitted on 1 Jul 2026]Title:Diffusion-GR2: Diffusion Generative Reasoning Re-ranker
Authors:Zhuoxuan Zhang, Kangqi Ni, Yuhang Chen, Mingfu Liang, Xiaohan Wei, Yunchen Pu, Fei Tian, Chonglin Sun, Frank Shyu, Adam (Yang)Song, Sandeep Pandey, Luke Simon, Tianlong Chen, Xi Liu View a PDF of the paper titled Diffusion-GR2: Diffusion Generative Reasoning Re-ranker, by Zhuoxuan Zhang and 13 other authors View PDF HTML (experimental)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. To reduce this cost, block-diffusion language models decode many positions in parallel over a few denoising steps and are substantially faster, yet naively converting an AR re-ranker into one opens two accuracy gaps: (1) a structural gap: answer positions are denoised in parallel and scored independently, so the decoder emits invalid rankings (duplicated, dropped, or out-of-set identifiers) that AR avoids through left-to-right masking; and (2) a distributional gap: fine-tuning the converted model on fixed teacher trajectories is off-policy relative to its own decoding at inference, leaving a residual accuracy gap. To close both gaps while keeping the speedup, we propose \textbf{Diffusion-GR2}, a recipe that converts our AR reasoning re-ranker (GR2) into a block-diffusion re-ranker. First, conversion fine-tuning (CFT) adapts the AR-initialized diffusion model to denoise the answer into a valid permutation on its own, without an external constrained decoder. Next, on-policy distillation (OPD) then supervises the model on its own decoded trajectories with dense per-token targets from the AR teacher. Finally, we apply a reinforcement-learning (RL) stage against a re-ranking reward on top of OPD's on-policy policy. Experiments on Amazon Beauty demonstrate that Diffusion-GR2 recovers to near-parity with the AR re-ranker, while block-parallel decoding raises decode throughput by $2.4$--$3.5\times$ at the model's reasoning output length. Ablations show that CFT recovers most of the conversion gap, and that on-policy distillation further closes it to the AR reference.
| Comments: | Work in progress |
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.01170 [cs.IR] |
| (or arXiv:2607.01170v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01170 Focus to learn more arXiv-issued DOI via DataCite |
Submission history
From: Mingfu Liang [view email][v1] Wed, 1 Jul 2026 17:02:20 UTC (1,384 KB)
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