Netflix authors claim their GenRec LLM-backed ranker — verbalized member histories post-trained with ranking rewards on an in-house foundation model — achieved statistically significant offline and online A/B gains over the production discriminative ranker with far fewer labeled signals, and Netflix shipping it or other large-scale recommenders following would establish LLM-backed ranking in production.

Open article · Retrieved 2026-09-28T18:37:04.617664+00:00

# Computer Science > Information Retrieval

**arXiv:2608.10257** (cs)

[Submitted on 10 Aug 2026 ([v1](https://arxiv.org/abs/2608.10257v1)), last revised 21 Aug 2026 (this version, v2)]

# Title:GenRec: An LLM-Backed Recommendation Ranker at Netflix

Authors:[Ying Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+Y), [Shradha Sehgal](https://arxiv.org/search/cs?searchtype=author&query=Sehgal,+S), [Arjun Rao](https://arxiv.org/search/cs?searchtype=author&query=Rao,+A), [Rein Houthooft](https://arxiv.org/search/cs?searchtype=author&query=Houthooft,+R), [Yaochen Zhu](https://arxiv.org/search/cs?searchtype=author&query=Zhu,+Y), [Ashish Rastogi](https://arxiv.org/search/cs?searchtype=author&query=Rastogi,+A)

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> Abstract:Large language models (LLMs) are reshaping recommender systems by enabling richer modeling of users, content, and context directly in natural language. At Netflix, we are exploring this direction through GenRec, an LLM-backed recommendation ranker built on top of an in-house foundational LLM. GenRec follows a two-phase framework: Phase 1 adapts an open-source LLM to Netflix data, developing deep understanding of the catalog and member behavior while balancing capabilities such as content understanding and instruction following. Phase 2 post-trains this foundation model with recommendation-ranking specific data, labels, and reward signals, aiming to align the ranker with business requirements and long-term member satisfaction.
>   
> This paper focuses on Phase 2 and the transition from a traditional discriminative ranker with thousands of engineered features to an LLM-backed ranker driven by verbalized user histories and context. We describe our design for input verbalization and context engineering, post-training data construction, reward integration, model architecture, and a cost-constrained serving design based on a prefill-only inference approach. We report results from a large-scale A/B test comparing GenRec against the current production ranker model, where we show that a GenRec model trained with substantially fewer Phase-2 labeled training examples and input signals can achieve statistically significant gains in offline and online metrics. We discuss how LLM-backed recommenders could shift the recommendation paradigm: from feature engineering to context engineering, and from bespoke architectures to shared foundation backbones. We also outline practical lessons for serving such systems under real-world resource constraints.

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| --- |
| Comments: |
| Subjects: | Information Retrieval (cs.IR) |
| Cite as: | [arXiv:2608.10257](https://arxiv.org/abs/2608.10257) [cs.IR] |
|  | (or  [arXiv:2608.10257v2](https://arxiv.org/abs/2608.10257v2) [cs.IR] for this version) |
|  | <https://doi.org/10.48550/arXiv.2608.10257> Focus to learn more  arXiv-issued DOI via DataCite |

## Submission history

From: Ying Li [[view email](https://arxiv.org/show-email/a7a5002e/2608.10257)]

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