





















Authors:Jinbin Bai, Yixuan Li, Yuchen Zhu, Yi Xin, Qingyu Shi, Aosong Feng, Xiaohong Liu, Molei Tao, Jianru Xue, Xiangtai Li, Ming-Hsuan Yang
Abstract:Inference-time compute has re-emerged as a practical way to improve LLM reasoning. Most test-time scaling (TTS) algorithms rely on autoregressive decoding, which is ill-suited to discrete diffusion language models (dLLMs) due to their parallel decoding over the entire sequence. As a result, developing effective and efficient TTS methods to unlock dLLMs' full generative potential remains an underexplored challenge. To address this, we propose Prism (Pruning, Remasking, and Integrated Self-verification Method), an efficient TTS framework for dLLMs that (i) performs Hierarchical Trajectory Search (HTS) which dynamically prunes and reallocates compute in an early-to-mid denoising window, (ii) introduces Local branching with partial remasking to explore diverse implementations while preserving high-confidence tokens, and (iii) replaces external verifiers with Self-Verified Feedback (SVF) obtained via self-evaluation prompts on intermediate completions. Across four mathematical reasoning and code generation benchmarks on three dLLMs, including LLaDA 8B Instruct, Dream 7B Instruct, and LLaDA 2.0-mini, our Prism achieves a favorable performance-efficiency trade-off, matching best-of-N performance with substantially fewer function evaluations (NFE). The code is released at this https URL.
| Comments: | Accepted to ICML 2026. Codes and Supplementary Material: this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.01842 [cs.LG] |
| (or arXiv:2602.01842v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.01842 arXiv-issued DOI via DataCite |
From: Jinbin Bai [view email]
[v1]
Mon, 2 Feb 2026 09:14:51 UTC (1,326 KB)
[v2]
Mon, 16 Mar 2026 06:49:47 UTC (1,327 KB)
[v3]
Tue, 5 May 2026 12:24:00 UTC (1,324 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。