Frontiers

Artificial Intelligence in Education: Reinforcement Learning and Human-AI Collaboration in AI-Driven Education

The integration of artificial intelligence within education has led to a new era of personalized and adaptive learning, fundamentally changing classroom ...
IEEE

RLCoder: Reinforcement Learning for Repository-Level Code Completion

Abstract: Repository-level code completion aims to generate code for unfinished code snippets within the context of a specified repository. Existing approaches mainly rely on retrievalaugmented ...
IEEE

A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler

Abstract: Code optimization is a crucial task that aims to enhance code performance. However, this process is often tedious and complex, highlighting the necessity for automatic code optimization ...
marktechpost

A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a ...
Microsoft

UniRG: Scaling medical imaging report generation with multimodal reinforcement learning

AI can be used to produce clinically meaningful radiology reports using medical images like chest x-rays. Medical image report generation can reduce reporting burden while improving workflow ...
VentureBeat

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

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...
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 ...
GitHub

DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research

DR Tulu-8B is the first open Deep Research (DR) model trained for long-form DR tasks. DR Tulu-8B matches OpenAI DR on long-form DR benchmarks. Feburary 9, 2026: 🔥 We released a free interactive demo ...
VentureBeat

Google’s new AI training method helps small models tackle complex reasoning

Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning ...
marktechpost

How to Build, Train, and Compare Multiple Reinforcement Learning Agents in a Custom Trading Environment Using Stable-Baselines3

In this tutorial, we explore advanced applications of Stable-Baselines3 in reinforcement learning. We design a fully functional, custom trading environment, integrate multiple algorithms such as PPO ...
acm.org

Rediscovering Reinforcement Learning

Reinforcement learning (RL) is machine learning (ML) in which the learning system adjusts its behavior to maximize the amount of reward and minimize the amount of punishment it receives over time ...
acm.org

Shields for Safe Reinforcement Learning

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...