Abstract
Interaction between learner and feedback-giver has come into focus recently for post-training
of Large Language Models (LLMs), through the use of reward models that judge the appropriateness of a model’s response. In this paper, we investigate whether Dialogue Games—goaldirected and rule-governed activities driven predominantly by verbal actions—can also serve as a source of feedback signals for learning. We introduce PLAYPEN, an environment for offand online learning through Dialogue Game
self-play, and investigate a representative set of post-training methods: supervised fine-tuning;
direct alignment (DPO); and reinforcement learning with Group Relative Policy Optimization (GRPO). We experiment with post-training a small LLM (Llama-3.1-8B-Instruct), evaluating performance on unseen instances of training games as well as unseen games, and on standard benchmarks. We find that imitation
learning through SFT improves performance on unseen instances, but negatively impacts other skills, while interactive learning with GRPO shows balanced improvements without loss of skills. We release the framework and the baseline training setups to foster research in this promising new direction of “learning in (synthetic) interaction”