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What are RLVR environments for LLMs? | Policy, rollouts & rubrics explained

A clear breakdown of RLVR environments for LLMs — what they are, how policies and rollouts work, and the role of rubrics in ...

Core Chinese research team behind cutting-edge AI model R1 remains intact: DeepSeek

The 18 core scientists behind R1 continue to power the start-up's AI ambitions and capabilities amid growing anticipation of ...
Electronics360

Wind turbine control systems: From PID to reinforcement learning

In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...
eLife

A differentiable model for optimizing the genetic drivers of synaptogenesis

This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...
Mirage News

Autonomous Vehicle Safety: FNIRS Tracks Passenger States

In recent years, several serious traffic accidents have exposed the shortcomings of current autonomous driving systems in making safe decisions ...
Microsoft

Agent Lightning: Adding reinforcement learning to AI agents without code rewrites

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...
Frontiers

A combined approach to lithology identification using reinforcement learning and transformer algorithms

Lithology identification plays a pivotal role in logging interpretation during drilling operations, directly influencing drilling decisions and efficiency. Conventional lithology identification ...
IEEE

Online Reinforcement Learning Algorithm Design for Adaptive Optimal Consensus Control Under Interval Excitation

Abstract: This article proposes online data-based reinforcement learning (RL) algorithm for adaptive output consensus control of heterogeneous multiagent systems (MASs) with unknown dynamics. First, ...
marktechpost

Weak-for-Strong (W4S): A Novel Reinforcement Learning Algorithm that Trains a weak Meta Agent to Design Agentic Workflows with Stronger LLMs

W4S operates in turns. The state contains task instructions, the current workflow program, and feedback from prior executions. An action has 2 components, an analysis of what to change, and new Python ...
Frontiers

Intelligent maneuver decision-making for UAVs using the TD3–LSTM reinforcement learning algorithm under uncertain information

Aiming to address the complexity and uncertainty of unmanned aerial vehicle (UAV) aerial confrontation, a twin delayed deep deterministic policy gradient (TD3)–long short-term memory (LSTM) ...
IEEE

A TD3-Based Reinforcement Learning Algorithm With Curriculum Learning for Adaptive Yaw Control in All-Wheel-Drive Electric Vehicles

Abstract: A novel artificial intelligence-based approach for the direct yaw control (DYC) of an all-wheel drive (AWD) electric vehicle (EV) is proposed in this paper. To improve adaptability and ...