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Why Reinforcement Learning Matters Now The core idea behind reinforcement learning is for a system to learn in the same manner that people and animals learn—by taking actions and adjusting ...

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 ...

Computing pioneer Alan Turing suggested training machines with rewards and punishments. Two computer scientists put the idea into practice in the 1980s and set the stage for the likes of ChatGPT.

Reinforcement learning was perhaps most famously used by Google DeepMind in 2016 to build AlphaGo, a program that learned for itself how to play the incredibly complex and subtle board game Go to ...

When tested, DeepSeek-R1 scored 79.8% on AIME 2024 mathematics tests and 97.3% on MATH-500. It also achieved a 2,029 rating on Codeforces — better than 96.3% of human programmers.

Reinforcement learning techniques could be the keys to integrating robots — who use machine learning to output more than words — into the real world.

Reinforcement learning with human feedback is critical to not only ensuring the model’s alignment, it’s crucial to the long-term success and sustainability of generative AI as a whole.

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, ...

The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used ...