Research on adaptive course learning algorithmbased on reinforced feedback in English translationmodel optimization

The integration of symbolic reasoning with neural learning presents a promising avenue for improving the adaptability andinterpretability of English translation models. In this work, we propose a novel architecture, LogicAwareNet, which embedsdomain-specific logical constraints directly into the forward computation of neural networks. Unlike traditional models thattreat output dimensions independently, LogicAwareNet utilizes a logic-aware embedding mechanism and a compiled d-DNNFcircuit to ensure global consistency in structured predictions. This design enables the model to maintain both statisticalfluency and symbolic validity across outputs. Building on this foundation, we introduce Constraint-Aligned Optimization, adedicated training procedure that incorporates symbolic feedback into the parameter update loop. This approach goes beyondpenalty-based methods by integrating semantic residual signals, logic-informed gradients, and projection-based supervision,thereby guiding the model to adhere to both empirical data and formal constraints. Mechanisms such as structure-awaredropout, entropic sharpening, and curriculum scheduling enhance the model’s ability to learn from sparse or noisy feedback.Our method demonstrates significant improvements in translation accuracy and logical conformity, particularly in educationaland structured-output applications. By unifying neural learning and symbolic logic at both architectural and optimization levels,this framework offers a scalable and theoretically grounded path toward more robust and interpretable translation systems. Theproposed approach contributes broadly to the intersection of AI and linguistics, enabling models that not only translate but alsoreason.