In the digital realm, ensuring the security and reliability of systems and software is of paramount importance. Fuzzing has emerged as one of the most effective testing techniques for uncovering ...

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 ...

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 ...

Fine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, DeepSeek-R1, Qwen3, Gemma 3, TTS 2x faster with 70% less VRAM.

In this tutorial, we build an advanced agentic Deep Reinforcement Learning system that guides an agent to learn not only actions within an environment but also how to choose its own training ...

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 ...

The examples are nothing if not relatable: preparing breakfast, or playing a game of chess or tic-tac-toe. Yet the idea of learning from the environment and taking steps that progress toward a goal ...

Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes. Chen Tang, Ben Abbatematteo, Jiaheng Hu, Rohan Chandra, Roberto Martín-Martín, and Peter Stone. Annual Review of Control, ...

Large Language Models (LLMs) have significantly advanced natural language processing (NLP), excelling at text generation, translation, and summarization tasks. However, their ability to engage in ...

Abstract: Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across ...