AI discovers superior lattice reduction strategies, outperforming LLL algorithm

Researchers have developed a new method using deep reinforcement learning to discover superior strategies for the Lenstra-Lenstra-Lovász (LLL) algorithm, a fundamental tool in computer science for lattice basis reduction. By framing lattice reduction as a Markov Decision Process and employing an AlphaZero-style self-play pipeline with Monte Carlo Tree Search, they trained a policy named DeltaStar. This new strategy, developed using small-dimensional lattices, requires fewer operations than the traditional LLL algorithm and demonstrates zero-shot generalization to higher dimensions and unseen moduli without retraining. AI

IMPACT This research could lead to more efficient algorithms in cryptography and other fields relying on lattice reduction.

RANK_REASON The cluster describes a new research paper published on arXiv detailing a novel AI-driven method for discovering improved algorithms.

Read on Hugging Face Daily Papers →

• alphaXiv
• AlphaZero
• arXiv
• CatalyzeX
• DagsHub
• DeltaStar
• Gotit.pub
• Hugging Face
• IArxiv
• LLL
• Monte Carlo tree search
• ScienceCast
• deep reinforcement learning

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →