Children acquire language from far less input than language models, and social interaction is widely thought to be part of the reason. Dialogue games cannot fully replicate human interaction, but they offer a controllable environment for simulating situated language use and for studying how models learn from and through interaction. The LM Playschool Challenge builds on this idea, shifting language model training away from observational data towards multi-turn language use. Participants were free to post-train a language model of any size, using any data or method, with one objective in mind — to improve its performance on an unseen set of dialogue games. Our findings suggest that post-training on dialogue games effectively teaches game rules and structure and can support generalisation, especially for small models — the best system achieved gains of roughly seven and three times its baseline score on held-out instances of seen games and on unseen games, respectively. However, gains on unseen games were concentrated in a few games, and larger models improved mainly by achieving better completion rates. General ability was largely retained across submitted systems, owing to the fact that fine-tuning on dialogue games produces shorter responses — and in turn, trigger fewer formatting errors on static benchmarks. Nearly all submissions relied on supervised fine-tuning, often followed by preference optimisation, and none trained on the dense turn-level feedback offered by Playpen, leaving this an open direction for future iterations of the challenge.

We examine three evaluation paradigms: standard benchmarks (e.g., MMLU and BBH), interactive games (e.g., Signalling Games or Taboo), and cognitive tests (e.g., for working memory or theory of mind). First, we investigate which of the former two—benchmarks or games—is most effective at discriminating LLMs of varying quality. Then, inspired by human cognitive assessments, we compile a suite of targeted tests that measure cognitive abilities deemed essential for effective language use, and we investigate their correlation with model performance in benchmarks and games. Our analyses reveal that interactive games are superior to standard benchmarks in discriminating models. Causal and logical reasoning correlate with both static and interactive tests, while differences emerge regarding core executive functions and social/emotional skills, which correlate more with games. We advocate for the development of new interactive benchmarks and targeted cognitive tasks inspired by assessing human abilities but designed specifically for LLMs.

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—goal-directed 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 off- and 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”.