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This paper explores using a model's semantic ID hierarchy for off-policy evaluation in generative recommenders, showing that coarsening to code-prefix clusters improves estimation accuracy under production logging constraints.
PRQ-KMeans is a post-hoc tokenization method that improves residual quantization for semantic identifiers by removing global-mean components, refining centroids, and using projection residuals, achieving significant performance gains in industrial search and public recommendation benchmarks.
This paper proposes DASO, a tree-aware post-training method for generative recommendation that addresses difficulty mismatch in GRPO by profiling rollout groups and reallocating based on prefix-match depth, improving performance on public benchmarks.