RL for LLM Agents
RL: Reinforcement Learning · LLM: Large Language Model
Overview
This field studies how to train large language model agents to act over multiple steps in environments such as web browsers, coding tools, games, and simulated workplaces. Common approaches use policy-gradient methods such as PPO and GRPO, verifiable task rewards, and process-level credit assignment to connect outcomes to particular actions or turns. Other methods teach agents to plan hierarchically, manage memory and reusable skills, or coordinate multiple agents, often using simulators and environment-generated feedback. A central challenge is making learning stable and efficient when rewards are sparse and tasks are long, while ensuring gains transfer to new environments and do not come from exploiting flawed reward signals.
Latest papers
616 papers134 papers in the last four weeks, up 139% on the four weeks before. 0.8% of all new papers.