LoopGR: recurrent depth scales generative recommenders with 52% fewer parameters
_reachsumit · x · 2026-10-28
Paper LoopGR (arXiv:2610.34157) introduces a parameter-efficient scaling framework for generative recommendation:
- Method: a hierarchical recurrent depth mechanism alternates between a shared generative decoder and a lightweight context updater, adding computational depth without adding parameters.
- Inference: recurrent trajectory self-distillation distills a low-budget student from a high-budget teacher, aligning both predictive distributions and evolution directions.
- Results: outperforms a representative encoder-decoder baseline while cutting model size by up to 52%.
- Takeaway: recurrent depth is a new scaling dimension for industrial recommenders constrained by model size and inference cost.
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