ByteDance — OneTrans-V2: One Transformer for the Whole Recsys Cascade

ByteDance's OneTrans-V2 runs retrieval, pre-rank and fine-rank as one Transformer: +9.74% GMV per user and 3.2× QPS on the same hardware in a 50/50 A/B test.

Key takeaways:

• One model, cascade kept. The three stages become tasks of one causal Transformer that encodes the user's behavior sequence once per request. Each stage keeps its own candidate features and budget, and fine-rank distills into pre-rank inside the model.
• Results. Against the deployed cascade (OneTrans-GR + OneTrans-Lite + OneTrans), user-level 50/50 A/B: GMV per user +9.74%, active days +0.25%, 3.2× QPS on the same hardware despite more FLOPs per request.
• DCGR retrieval. The model first predicts a decision prefix (purchase level, value, discovery, organic vs sponsored) as non-linguistic tokens, then generates item semantic IDs. One model replaced three retrieval channels: 1.5× main-channel GMV, 2.2× new categories, 2.1× sponsored GPM.
• Where the speed comes from. Kernel fusion + FP8 (2.16× on fine-rank) times one shared user context (1.53×). SNT is a training change: 4.4× faster training, 95% less sequence storage.
• Note. The paper doesn't name the platform or give CIs, but it does define the baseline and A/B design. The text-like prefix and per-click delta cache were hosts' illustrations.

Chapters:

00:00 A 10% GMV lift from one model

01:26 How the recsys cascade works

03:59 Three models, three encodings

04:56 One shared Transformer + distillation

07:05 Mixture of experts and µP

09:38 Decision-conditioned generative retrieval

11:35 Sequence-native training

13:08 Results: GMV and throughput

15:00 Novelty score

16:00 Related: LinkedIn Feed SR

16:42 Caveats and the offline-testing dilemma

19:18 Recap and closing thought

Sources:

• OneTrans-V2: Unifying Retrieval, Pre-rank, and Fine-rank with One Transformer in Industrial Recommender (Cao et al., ByteDance; arXiv 2609.28589, Sep 2026)
• An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking (Feed SR) (LinkedIn, related; arXiv 2602.12354, Feb 2026)

Related episode: S2E3: Meta — Expected Bayesian Loss for Neutral Launches

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Made with AI-generated voices; curated by Peter Liu from the sources above.