Babysit A Language Model From Scratch: Interactive Language Learning by Trials and Demonstrations (2025)

Abstract: Humans are efficient language learners and inherently social creatures. Our language development is largely shaped by our social interactions, for example, the demonstration and feedback from caregivers. Contrary to human language learning, recent advancements in large language models have primarily adopted a non-interactive training paradigm, and refined pre-trained models through feedback afterward. In this work, we explore how corrective feedback from interactions influences neural language acquisition from scratch through systematically controlled experiments, assessing whether it contributes to word learning efficiency in language models. We introduce a trial-and-demonstration (TnD) learning framework that incorporates three distinct components: student trials, teacher demonstrations, and a reward conditioned on language competence at various developmental stages. Our experiments reveal that the TnD approach accelerates word acquisition for student models of equal and smaller numbers of parameters, and we highlight the significance of both trials and demonstrations. We further show that the teacher’s choices of words influence students’ word-specific learning efficiency, and a practice-makes-perfect effect is evident by a strong correlation between the frequency of words in trials and their respective learning curves. Our findings suggest that interactive language learning, with teacher demonstrations and active trials, can facilitate efficient word learning in language models.

## introduction humans are social beings and we learn language from interactions @xcite @xcite @xcite . long before children's linguistic skills are mature, they could engage in early forms of conversational exchange with others @xcite * authors contributed equally to this work. @xcite . a critical component of social interactions that language grounds to is the feedback provided by the caregivers @xcite ). this, for example, includes the communicative feedback @xcite ) that highlights the success and failure of communication, and the corrective feedback @xcite @xcite that is more direct and emphasizes the responses from caregivers, which offer corrections to possible errors in children's speech, encompassing variants like negative evidence, reformulations, or recasts. unlike human learners who acquire language skills through feedback during interactions, most language models differ in terms of their inductive biases and data sources @xcite . these models typically learn from massive text corpora using cross-entropy loss for self-supervised learning. recently, several lines of cognitively motivated language modeling research have looked into the learnability and learning efficiency of language @xcite @xcite . by incorporating nonlinguistic inputs such as multimodal stimuli @xcite @xcite and/or communicative feedback @xcite @xcite , recent studies have explored potential mechanisms that contribute to efficient language learning in (vision-)language models. through controlled ablation studies @xcite , these models can serve as proof of concept to verify mechanisms that are practically effective for machines, and generate hypotheses that are possible for cognitive learners @xcite . in a similar spirit, we seek to investigate the role of explicit corrective feedback in neural language learning through controlled computational experiments. rather than making direct comparisons to human learning, our goal is to examine if student trials and teacher demonstrations promote efficient word learning in language model training, and if so, which words benefit the most. we introduce trial-and-demonstration (tnd), an interactive learning framework that incorporates corrective feedback with three components: student model trials, teacher model demonstrations, and a reward conditioned on the training trajectory of the model (figure 1 ). our experiments reveal that the tnd approach accelerates word acquisition, highlighting the significance of both trials and demonstrations. from the teacher's perspective, their word choices affect students' word-specific learning efficiency. from the student's side, the word frequency in trials closely aligns with the learning curve, supporting the idea that learning by language production accelerates word proficiency. our findings highlight that trials and demonstrations can facilitate word learning in language models, and further, suggest an efficient alternative to building language models interactively. ## interactive language learning by trials-and-demonstrations (tnd) modeling corrective feedback in computational language learning presents significant challenges, as recruiting human subjects to supervise the development of a language model from the ground up over numerous iterations is impractical. consequently, we present a human-free trial-and-demonstration (tnd) learning framework that streamlines the process (figure 1 ). in a scenario of corrective feed-back, the student model engages in productionbased learning: to produce an initial utterance, followed by the teacher model generating its version of the text as a demonstration. for the student model to recognize the teacher's response as preferable and to facilitate learning, these language outputs are evaluated by a reward function, which is based on the competence of the student's language use that is expected for its developmental stage (i.e., training steps). the tnd framework thus includes three components: a student model's trials, a teacher model's demonstrations, and rewards. this framework allows us to incorporate massive corpora to study modern generative language models, offering a general and unrestricted approach to simulate interactive language learning with corrective feedback on massive corpora. ## conclusion this research introduces a trial-and-demonstration (tnd) learning framework to examine the effectiveness of corrective feedback in neural word acquisition through systematically controlled experiments, assessing how the interplay between student trials and teacher demonstrations contributes to learning efficiency in neural language models. we find that (1) tnd learning accelerates neural word acquisition across student models of different sizes;