Social interaction is central to children’s language learning, but the effects of different forms of caregiver feedback are difficult to isolate in naturalistic data. We use child-like language models as controlled learners to test which forms of feedback support grammatical development. Small GPT-2-style models are pretrained on child-directed language from CHILDES, then fine-tuned with reinforcement learning using reward models trained to capture four feedback types: communicative feedback, structural alignment, semantic contingency, and affective feedback. Reward fine-tuning yields limited gains on minimal-pair evaluations, but clearer effects in free generation. Structural alignment produces the strongest improvements in grammaticality, while communicative feedback yields more moderate gains. In contrast, semantic contingency and affective feedback do not improve grammaticality. These results suggest that caregiver feedback is not uniformly beneficial: different forms of social feedback support different aspects of language learning.