Hi authors,
We recently released a preprint on efficient test-time scaling for discrete diffusion language models (dLLMs), titled:
PRISM: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models

PRISM is an test-time scaling framework that integrates:
- Hierarchical Trajectory Search (HTS): dynamically prunes/reallocates compute in an early-to-mid denoising window
- Local branching via partial remasking: explores diverse realizations while preserving high-confidence tokens
- Self-Verified Feedback (SVF): uses the same dLLM as a lightweight Yes/No verifier on intermediate completions (no external RM required)
We evaluate PRISM on three dLLMs, including LLaDA-2.0-mini. Would you consider adding a short note in the README under “Decoding / Inference / Test-time scaling” pointing users to PRISM as a tested decoding recipe for LLaDA-2.0-mini?
Suggested README snippet (feel free to edit):
Test-time scaling (PRISM)
Thanks for considering!
Hi authors,
We recently released a preprint on efficient test-time scaling for discrete diffusion language models (dLLMs), titled:
PRISM: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models
PRISM is an test-time scaling framework that integrates:
We evaluate PRISM on three dLLMs, including LLaDA-2.0-mini. Would you consider adding a short note in the README under “Decoding / Inference / Test-time scaling” pointing users to PRISM as a tested decoding recipe for LLaDA-2.0-mini?
Suggested README snippet (feel free to edit):
Test-time scaling (PRISM)
Thanks for considering!