SmartGR distills generative recommendation with 2.39× faster inference
_reachsumit · x · 2026-08-04
SmartGR distills a generative recommender into a faster student and reports a 2.39× speedup
This paper targets the cost of large generative recommendation models. Instead of scaling the teacher, it distills knowledge into a lightweight student while trying to preserve ranking quality.
Two problems the paper addresses
- Imbalanced difficulty across the semantic ID hierarchy
- Incorrect prefix pruning during beam search
Proposed approach
- Hierarchy-Aware SID Distillation transfers the teacher’s capability across the semantic hierarchy.
- Beam-Aware Ranking Distillation preserves the teacher’s ranking preferences during beam search.
Results
- Evaluated on four benchmark datasets.
- The authors report 8.6% performance improvement on average.
- They also report 2.39× average inference speedup.
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