Ever since researchers began noticing a slowdown in improvements to large language models using traditional training methods, ...

The Chief Digital and Artificial Intelligence Office of the Defense Department has announced it will award Anthropic, Google, OpenAI, and xAI contracts worth up to $200 million each "to develop ...

The new agent, called Asimov, was developed by Reflection, a small but ambitious startup cofounded by top AI researchers from ...

Through utilizing learning theory, Foal NZ has completed over 35,000 training sessions without injury during the past two ...

Agentic AI systems are revolutionizing how organizations approach complex workflows, introducing autonomous agents capable of multi-step reasoning, decision-making, and task execution that operate ...

When someone starts a new job, early training may involve shadowing a more experienced worker and observing what they do ...

However, discarding reinforcement learning meant, "something was lost in this transition: an agent's ability to self-discover its own knowledge," they write.

4. Reinforcement Learning Updates Finally, you'll need to use the filtered, high-quality feedback to fine-tune AI models.

A recent approach, Reinforcement Learning from Human Feedback (RLHF), has brought remarkable improvements to large language models (LLMs) by incorporating human preferences into the training process.

By balancing self-reinforcement with critical external feedback, we can optimize learning systems to foster deep, lasting expertise while avoiding the traps of unsupervised overconfidence.

Critique-based feedback methods have also been explored, with some utilizing self-generated critiques to improve generation quality or serve as preference signals. However, these approaches differ ...

Reinforcement Learning from Human Feedback (RLHF) has emerged as a crucial technique for enhancing the performance and alignment of AI systems, particularly large language models (LLMs). By ...