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.

Current approaches to embodied AI tend to learn policies from expert demonstrations. However, without a mechanism to evaluate the quality of demonstrated actions, they are limited to learning from optimal behaviour or risk replicating errors and inefficiencies. While reinforcement learning offers one alternative, the associated exploration typically results in sacrificing data efficiency. This work explores how agents trained with imitation learning can learn robust representations from both optimal and suboptimal demonstrations when given access to constructive language feedback as a means to contextualise different modes of behaviour. We directly provide language feedback embeddings as part of the input sequence into a Transformer-based policy, and optionally complement the traditional next action prediction objective with auxiliary self-supervised learning objectives for feedback prediction. We test our approach on a range of embodied Vision-and-Language tasks in our custom BabyAI-XGen environment and show significant improvements in agents’ compositional generalisation abilities and robustness, suggesting that our data-efficient method allows models to successfully convert suboptimal behaviour into learning opportunities. Overall, our results suggest that language feedback is a competitive and intuitive alternative to intermediate scalar rewards for language-specified embodied tasks.