Reinforcement learning has long been one of artificial intelligence's most promising yet an under explored fields. This is the technology behind the most incredible AI achievements, from algorithms ...
Discover Andrej Karpathy's insights on AI agents, LLMs, and economic growth. Insights on memory, education, and economic ...
Prediction of crystal structures of organic molecules is a critical task in many industries, especially in pharmaceuticals ...
In recent years, the field of robotics has undergone significant transformation, driven increasingly by advances in brain-inspired and neurally grounded ...
A survey of reasoning behaviour in medical large language models uncovers emerging trends, highlights open challenges, and introduces theoretical frameworks that enhance reasoning behaviour ...
Featuring AI-powered role-play simulations, the app allows learners to practise recognising distress and offering empathetic ...
For a long time, the core idea in reinforcement learning (RL) was that AI agents should learn every new task from scratch, like a blank slate. This "tabula rasa" approach led to amazing achievements, ...
Researchers at the Massachusetts Institute of Technology (MIT) are gaining renewed attention for developing and open sourcing ...
By teaching models to reason during foundational training, the verifier-free method aims to reduce logical errors and boost ...
Handling extremely long documents remains a persistent challenge for large language models (LLMs). Even with techniques such as length extrapolation and sparse attention, models often suffer from ...
ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...
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