LoopCTR trains CTR models with recursive layer reuse and process supervision so that zero-loop inference outperforms baselines on public and industrial datasets.
Dhen: A deep and hierarchical ensemble network for large-scale click-through rate prediction
11 Pith papers cite this work, alongside 8 external citations. Polarity classification is still indexing.
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Releases TencentGR-1M and TencentGR-10M datasets with baselines for all-modality generative recommendation in advertising, including weighted evaluation for conversions.
A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on index nodes.
Speculative precomputation of foundation-model user–item embeddings decouples heavy inference from the serving path and yields 0.67% revenue gain at Meta ads scale.
PyTorch Fully Sharded Data Parallel enables training of significantly larger models than Distributed Data Parallel with comparable speed and near-linear TFLOPS scaling.
GR2 applies mid-training on semantic IDs, reasoning distillation, RL with conditional verifiable rewards, and a context compressor to re-ranking in industrial recsys, reporting +18.7% R@1 over baselines.
DeMix diagnoses mixed error types in training data via influence-vector-based multi-label classification with an intervention strategy, reporting 22.61% F1 gain and 9.32% downstream improvement on 11 tasks.
Memento applies personalized RAG-style retrieval to long user history for Meta ads models, delivering 5-10x efficiency, sub-10ms latency, and 1% CTR / 1.2% CVR lifts in production.
LoKA claims to make FP8 practical for large recommendation models via statistical probing, model adaptations, and accuracy-aware kernel dispatch.
Empirical scaling of backbone, embeddings, and data shows largely independent additive gains, enabling a deployed model with 2.5x data and 8x compute that delivers +2.6% CVR improvement with minimal latency change.
citing papers explorer
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LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction
LoopCTR trains CTR models with recursive layer reuse and process supervision so that zero-loop inference outperforms baselines on public and industrial datasets.
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Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation
Releases TencentGR-1M and TencentGR-10M datasets with baselines for all-modality generative recommendation in advertising, including weighted evaluation for conversions.
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Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation
A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on index nodes.
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SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling
Speculative precomputation of foundation-model user–item embeddings decouples heavy inference from the serving path and yields 0.67% revenue gain at Meta ads scale.
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PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
PyTorch Fully Sharded Data Parallel enables training of significantly larger models than Distributed Data Parallel with comparable speed and near-linear TFLOPS scaling.
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GR2 Technical Report
GR2 applies mid-training on semantic IDs, reasoning distillation, RL with conditional verifiable rewards, and a context compressor to re-ranking in industrial recsys, reporting +18.7% R@1 over baselines.
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DeMix: Debugging Training Data with Mixed Data Error Types by Investigating Influence Vectors
DeMix diagnoses mixed error types in training data via influence-vector-based multi-label classification with an intervention strategy, reporting 22.61% F1 gain and 9.32% downstream improvement on 11 tasks.
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Memento: Personalized RAG-Style Long-Retention Data Scaling for META Ads Recommendation
Memento applies personalized RAG-style retrieval to long user history for Meta ads models, delivering 5-10x efficiency, sub-10ms latency, and 1% CTR / 1.2% CVR lifts in production.
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LoKA: Low-precision Kernel Applications for Recommendation Models At Scale
LoKA claims to make FP8 practical for large recommendation models via statistical probing, model adaptations, and accuracy-aware kernel dispatch.
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On the Practice of Scaling Search Conversion Rate Prediction
Empirical scaling of backbone, embeddings, and data shows largely independent additive gains, enabling a deployed model with 2.5x data and 8x compute that delivers +2.6% CVR improvement with minimal latency change.
- FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation