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.

Negotiation is a fundamental challenge for AI agents, as it requires an ability to reason strategically, model opponents, and balance cooperation with competition. We present the first comprehensive study that systematically evaluates how explicit reasoning training affects the negotiation abilities of both commercial and open-weight large language models, comparing these models to their vanilla counterparts across three languages. Using a self-play setup across three diverse dialogue games, we analyse trade-offs between performance and cost, the language consistency of reasoning processes, and the nature of strategic adaptation exhibited by models.Our findings show that enabling reasoning—that is, scaling test time compute—significantly improves negotiation outcomes by enhancing collaboration and helping models overcome task complexities, but comes at a substantial computational cost: reasoning improves GPT-5’s performance by 31.4 % while increasing its cost by nearly 400 %. Most critically, we uncover a significant multilingual reasoning distinction: open-weight models consistently switch to English for their internal reasoning steps, even when negotiating in German or Italian (and thus possibly impacting potential explainability gains through the disclosure of reasoning traces), while a leading commercial model maintains language consistency between reasoning and final output.