Customizing multiturn AI agents with reinforcement learning
Leveraging existing environment simulators and reward functions based on verifiable ground truth boosts task success rate, even with small models and small trai...
9 posts from this source
Leveraging existing environment simulators and reward functions based on verifiable ground truth boosts task success rate, even with small models and small trai...
A new hybrid optimization approach allows edge devices to fine-tune vision-language models using only forward passes, achieving up to 7% higher accuracy than ex...
'Reinforcement learning gyms' train agents on the many low-level tasks that they must chain together to execute customer requests....
From foundation model safety frameworks and formal verification at cloud scale to advanced robotics and multimodal AI reasoning, these are the most viewed publi...
From quantum computing breakthroughs and foundation models for robotics to the evolution of Amazon Aurora and advances in agentic AI, these are the posts that c...
New audio-processing technology is making entertainment more accessible for millions of viewers....
New service lets customers mix their own data with the data used to train Amazon Nova at each major stage of model development, enabling deep domain understandi...
A multiagent architecture separates data perception, tool knowledge, execution history, and code generation, enabling ML automation that works with messy, real-...
“Network language models” will coordinate complex interactions among intelligent components, computational infrastructure, access points, data centers, and more...