Nvidia’s new technique cuts LLM reasoning costs by 8x without losing accuracy

Nvidia researchers developed dynamic memory sparsification (DMS), a technique that compresses the KV cache in large language models by up to 8x while maintaining reasoning accuracy — and it can be ...
InfoQ

AI Researchers Improve LLM-Based Reasoning by Mimicking Learning from Mistakes

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Cory Benfield discusses the evolution of ...
NextBigFuture

LLM Scale and Solving of Programming as a Path to Superintelligence

Yann LeCun’s argues that there are limitations of chain-of-thought (CoT) prompting and large language model (LLM) reasoning. LeCun argues that these fundamental limitations will require an entirely ...
VentureBeat

Meta researchers open the LLM black box to repair flawed AI reasoning

Researchers at Meta FAIR and the University of Edinburgh have developed a new technique that can predict the correctness of a large language model's (LLM) reasoning and even intervene to fix its ...
NextBigFuture

Reinforcement Learning Does NOT Fundamentally Improve AI Models

Reinforcement Learning does NOT make the base model more intelligent and limits the world of the base model in exchange for early pass performances. Graphs show that after pass 1000 the reasoning ...
Hosted on MSN

China's DeepSeek applying trial-and-error learning to its AI 'reasoning'

Chinese AI company DeepSeek has shown it can improve the reasoning of its LLM DeepSeek-R1 through trial-and-error based reinforcement learning, and even be made to ...
Diginomica

Enterprise hits and misses - AI agents need definition, Apple issues fresh data on LLM reasoning flaws, and a tech CEO hammers return to office mandates

Something was bothering Ian Thomas about AI agents - and it wasn't just the hype. It was the confusion over what agents actually do (agent-washing by vendors doesn't help either). So he wrote a ...