Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025)

Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...

How Google’s 'internal RL' could unlock long-horizon AI agents

Google researchers introduce ‘Internal RL,’ a technique that steers an models' hidden activations to solve long-horizon tasks ...

Apple AI research shows how MLLMs understand, generate, search for images

Apple's researchers continue to focus on multimodal LLMs, with studies exploring their use for image generation, ...
IEEE

Enhanced Reward Function Design for Source Term Estimation Based on Deep Reinforcement Learning

Abstract: This study investigates the design of reward functions for deep reinforcement learning-based source term estimation (STE). Estimating the properties of unknown hazardous gas leakage using a ...
Carnegie Mellon University

What a Virtual Zebrafish Can Teach Us About Autonomous AI

Aran Nayebi, an assistant professor in the Machine Learning Department in CMU's School of Computer Science, is part of a research team that created a virtual zebrafish that acted like a real zebrafish ...
MIT Technology Review

Meet the new biologists treating LLMs like aliens

By studying large language models as if they were living things instead of computer programs, scientists are discovering some ...
IEEE

Preference-Based Multi-Objective Reinforcement Learning

Abstract: Multi-objective reinforcement learning (MORL) is a structured approach for optimizing tasks with multiple objectives. However, it often relies on pre-defined reward functions, which can be ...