Abstract: In spite of the fact that Reinforcement Learning (RL) training has contributed to massive gains in Large Language Model (LLM) abilities, it is largely still limited to optimizing a single response to a user query, rather than learning how to plan the course of a conversation or interaction, or interacting with other agents that may change their behavior during training and deployment. This talk will discuss recent work that enables both multi-agent and multi-turn RL post training. On the multi-turn side, we address critical challenges in long-horizon interaction: for example, we introduces a curiosity-based intrinsic reward that enables LLMs to learn how to learn about the user, significantly improving both personalization and online generalization to new users. I will also discuss Generative Adversarial Post Training (GAPT), an adversarial RL framework which draws from GANs, and is designed to mitigate reward hacking and output collapse in creative, adaptive tasks where preserving diversity and realism is paramount. Finally, I will discuss our group’s work on multi-agent interactive training, which can provide both safety guarantees, and the emergence of complex skills. Together, these methods demonstrate novel approaches to instilling complex, user-aware planning capabilities and safeguarding output quality over extended multi-agent interactions.