“This demonstrates the feasibility of using a well-trained LLM as a substitute for real search engines in reinforcement learning setups,” the paper notes. What this means for the future of AI ...

For organizations with clearly defined problems and verifiable answers, RFT offers a compelling way to align models.

Teaching AI to explore its surroundings is a bit like teaching a robot to find treasure in a vast maze—it needs to try different paths, but some lead nowhere. In many real-world challenges, like ...

Similar to the process of supervised learning, where AI models are nudged closer to the correct answer with pre-labeled data sets, reinforcement ... s o3-mini model using scientific literature ...

This study presents a valuable finding on how the locus coeruleus modulates the involvement of medial prefrontal cortex in set shifting using calcium imaging. The evidence supporting the claims was ...

One is to use reinforcement learning, where robots learn by interacting in the same environment with millions to billions of trials and errors, which is inefficient with no guarantee of success.

This paper presents a groundbreaking approach to behavior modeling and enhancement for biological hybrid cockroach robots using reinforcement learning (RL). The study focuses on developing a hybrid ...

We propose a novel Smooth Maximum Entropy Deep Inverse Reinforcement Learning (S-MEDIRL) algorithm that can extrapolate ... The trajectory sampled from the learned cost map is then executed using a ...