Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models
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 https://github.com/viiika/Prism.
69817c1cce18b1862809612b
Jinbin Bai
claimed_verified
2026-02-03T10:06:41.260Z
63fccdac93b993a4ebd7789a
https://cdn-avatars.huggingface.co/v1/production/uploads/63fccdac93b993a4ebd7789a/KRx8vpdoDjsZBRw0j8Vg8.jpeg
Jinbin Bai
BryanW
user
BryanW
69817c1cce18b1862809612c
Yixuan Li
69817c1cce18b1862809612d
Yuchen Zhu
69817c1cce18b1862809612e
Yi Xin
69817c1cce18b1862809612f
Qingyu Shi
69817c1cce18b18628096130
Aosong Feng
69817c1cce18b18628096131
Xiaohong Liu
69817c1cce18b18628096132
Molei Tao
69817c1cce18b18628096133
Jianru Xue
69817c1cce18b18628096134
Xiangtai Li
69817c1cce18b18628096135
Ming-Hsuan Yang
Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models
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 https://github.com/viiika/Prism.
A new test-time scaling framework called Prism is introduced for discrete diffusion language models that improves reasoning performance through hierarchical trajectory search, local branching with partial remasking, and self-verified feedback mechanisms.
discrete diffusion language models test-time scaling autoregressive decoding denoising window hierarchical trajectory search local branching partial remasking self-verified feedback mathematical reasoning code generation