InfoWorld

Multi-token prediction technique triples LLM inference speed without auxiliary draft models

With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale.

Researchers baked 3x inference speedups directly into LLM weights — without speculative decoding

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...
GitHub

Awesome RL-based Agentic Search Papers

2026.1 BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search Adaptive Search Decision; Reliability / IDK Boundary arXiv Code 2026.1 Agentic-R: Learning to Retrieve for Agentic Search ...