Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment

Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment

Training Diffusion Models with Reinforcement Learning We deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone. Our goal is to tackle “stop-and-go” waves, those frustrating slowdowns and speedups that usually have no clear cause but lead to congestion and significant energy waste. To train efficient…

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Reinforcement Learning Meets Chain-of-Thought: Transforming LLMs into Autonomous Reasoning Agents

Reinforcement Learning Meets Chain-of-Thought: Transforming LLMs into Autonomous Reasoning Agents

Large Language Models (LLMs) have significantly advanced natural language processing (NLP), excelling at text generation, translation, and summarization tasks. However, their ability to engage in logical reasoning remains a challenge. Traditional LLMs, designed to predict the next word, rely on statistical pattern recognition rather than structured reasoning. This limits their ability to solve complex problems…

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