Difficulty-Aware Curriculum and Token-Weighted Rationale Distillation from LLM for Sequential Recommendation

While large language models (LLMs) excel at generating explanations in real-world recommendation scenarios, the constraints on response latency and deployment costs limit their practical use. As a solution, Knowledge Distillation has emerged as an efficient paradigm, providing a viable path to migrate the capabilities of LLMs into compact student models. However, transferring LLMs’ abilities for preference inference and long-form explanation generation into a lightweight student model still remains a challenge. First, there is substantial variation in the difficulty of analyzing user historical interaction records. Without considering the different levels of difficulty, the student model may learn rough and even inaccurate rationales. Second, the explanatory text itself is often noisy and tends to be generic. If the student model is trained directly to imitate the outputs of the teacher model, it tends to learn formulaic expressions rather than the underlying reasoning process. To address these issues, we present a rationale distillation framework that employs rationale generated by a teacher model as structured supervision. Particularly, we estimate difficulty scores from history-embedding clustering statistics and adopt an easy-to-hard curriculum that progressively introduces harder, i.e., more ambiguous instances. To reduce noise in teacher-generated rationales, we learn their token weights using a weight generator. The generator is trained with simple regularization and updated iteratively so that the weights remain effective as the student model evolves. Finally, we train the student model with a dual-weighted loss that includes both the instance difficulty and the token weights. Experiments on the MovieLens and Amazon Electronics datasets show that distilling Qwen2.5-32B into Qwen2.5-3B achieves satisfactory explanation as well as effective ranking performance for sequential recommendation with lower inference costs. ( ^1 The code is available at https://github.com/HduDBSI/RDistillRec .)