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cs.LG updates on arXiv.org

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Prism: Efficient Test-Time Scaling via Hierarchical Searc...
Jinbin Bai, · 2026-05-06 · via cs.LG updates on arXiv.org

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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

Submission history

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)