[RL] Reinforcement Learning for Large Reasoning Models (LRMs/ LLM): A Survey. скачать в хорошем качестве

[RL] Reinforcement Learning for Large Reasoning Models (LRMs/ LLM): A Survey.

Reinforcement Learning for Large Reasoning Models: A Survey This survey explores recent advancements in Reinforcement Learning (RL) for reasoning with Large Language Models (LLMs), particularly as they evolve into Large Reasoning Models (LRMs) for tasks like mathematics and coding. It examines foundational elements such as reward design, including verifiable, generative, dense, and unsupervised rewards, along with different policy optimization algorithms like critic-based and critic-free approaches. The text also covers training resources, from static datasets to dynamic environments and specialized RL infrastructure, and highlights key applications in areas such as coding, agentic tasks, multimodal understanding and generation, multi-agent systems, robotics, and medical reasoning. Finally, it addresses future research directions and persistent challenges related to scalability, stability, and generalization in this rapidly developing field.