The Challenge: Scaling Autonomous Agents with RL Autonomous AI agents have been at the forefront of taking computational abilities to various real-world tasks, and reinforcement learning (RL) is the key approach in agent creation. RL involves helping computational agents learn…

Sakana AI introduces a novel framework for reasoning language models (LLMs) with a focus on efficiency and reusability: Reinforcement-Learned Teachers (RLTs). Traditional reinforcement learning (RL) approaches in LLMs are plagued by sparse reward signals and prohibitively high computational demands. By…

In this tutorial, we guide users through building a robust, production-ready Python SDK. It begins by showing how to install and configure essential asynchronous HTTP libraries (aiohttp, nest-asyncio). It then walks through the implementation of core components, including structured response…

Redefining Job Execution with AI Agents AI agents are reshaping how jobs are performed by offering tools that execute complex, goal-directed tasks. Unlike static algorithms, these agents combine multi-step planning with software tools to handle entire workflows across various sectors,…

A large language model (LLM) deployed to make treatment recommendations can be tripped up by nonclinical information in patient messages, like typos, extra white space, missing gender markers, or the use of uncertain, dramatic, and informal language, according to a…

LLM-Based Code Generation Faces a Verification Gap LLMs have shown strong performance in programming and are widely adopted in tools like Cursor and GitHub Copilot to boost developer productivity. However, due to their probabilistic nature, LLMs cannot provide formal guarantees…