Lydfilen for denne episode er muligvis beskadiget
How to fix it?
OM DENNE EPISODE
The authors of the new paper *Self-Adapting Language Models (SEAL)* shared a behind-the-scenes look at their work, motivations, results, and future directions.
The paper introduces a novel method for enabling large language models (LLMs) to adapt their own weights using self-generated data and training directives — “self-edits.”
Learn more about the Self-Adapting Language Models paper.
Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.
Engelsk
USA
SØG BLANDT TIDLIGERE EPISODER
Søg efter gamle afsnit af Deep Papers.
ANDRE EPISODE I DENNE PODCAST
In this week's episode, we talk about Elastic Reasoning, a novel framework designed to enhance the efficiency and scalability of large reasoning models by explicitly separating the reasoning process into two distinct phases: thinking and solution.
This separation allows for independent alloca…
We discuss Accurate KV Cache Quantization with Outlier Tokens Tracing, a deep dive into improving the efficiency of LLM inference. The authors enhance KV Cache quantization, a technique for reducing memory and compute costs during inference, by introducing a method to identify and exclude outlier t…
What if your LLM could think ahead—preparing answers before questions are even asked?
In this week's paper read, we dive into a groundbreaking new paper from researchers at Letta, introducing sleep-time compute: a novel technique that lets models do their heavy lifting offline, well before the…
This week we discuss The Illusion of Thinking, a new paper from researchers at Apple that challenges today’s evaluation methods and introduces a new benchmark: synthetic puzzles with controllable complexity and clean logic.
Their findings? Large Reasoning Models (LRMs) show surprising failure mode…
Ansvarsfraskrivelse: Podcasten og kunstværket, der er indlejret på denne side, er fra Arize AI, som tilhører dens ejer og ikke er tilknyttet eller godkendt af Listen Notes, Inc.
REDIG
Tak fordi du hjælper med at holde podcast-databasen opdateret.