Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking

• URL: http://arxiv.org/abs/2604.07420v1
• Date: Wed, 08 Apr 2026 14:54:10 GMT
• Title: Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking
• Abstract summary: Kuaishou serves over 400 million daily active users, processing hundreds of millions of search queries daily.<n>As the final decision layer, the reranking stage determines user experience by optimizing whole-page utility.<n>We propose Dual-Rerank, a unified framework designed for industrial reranking.
• Score: 11.52944506792799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Kuaishou serves over 400 million daily active users, processing hundreds of millions of search queries daily against a repository of tens of billions of short videos. As the final decision layer, the reranking stage determines user experience by optimizing whole-page utility. While traditional score-and-sort methods fail to capture combinatorial dependencies, Generative Reranking offers a superior paradigm by directly modeling the permutation probability. However, deploying Generative Reranking in such a high-stakes environment faces a fundamental dual dilemma: 1) the structural trade-off where Autoregressive (AR) models offer superior Sequential modeling but suffer from prohibitive latency, versus Non-Autoregressive (NAR) models that enable efficiency but lack dependency capturing; 2) the optimization gap where Supervised Learning faces challenges in directly optimizing whole-page utility, while Reinforcement Learning (RL) struggles with instability in high-throughput data streams. To resolve this, we propose Dual-Rerank, a unified framework designed for industrial reranking that bridges the structural gap via Sequential Knowledge Distillation and addresses the optimization gap using List-wise Decoupled Reranking Optimization (LDRO) for stable online RL. Extensive A/B testing on production traffic demonstrates that Dual-Rerank achieves State-of-the-Art performance, significantly improving User satisfaction and Watch Time while drastically reducing inference latency compared to AR baselines.

Related papers

• DaV-Gen: End-to-End Generative Retrieval via Draft-and-Verify [4.546259713278263]
  textbfDaV-Gen is a novel unified solution designed to fundamentally the paradigm for both search and recommendation.<n>During training, the model is concurrently optimized for both candidate drafting and fine-grained verification.<n>This is achieved by a composite loss function that jointly trains the model on two distinct but related objectives.
  arXiv  Detail & Related papers  (2026-07-09T11:22:50Z)
• Think When Needed: Adaptive Reasoning-Driven Multimodal Embeddings with a Dual-LoRA Architecture [5.59567612070537]
  Think When Needed (TWN) is a unified multimodal embedding framework with adaptive reasoning.<n>Building on this, an adaptive think mechanism uses a self-supervised routing gate to decide per input whether to generate chain-of-thought (CoT)<n>On the 78 tasks of MMEB-V2, TWN achieves state-of-the-art embedding quality while being substantially more efficient than existing generative methods.
  arXiv  Detail & Related papers  (2026-05-14T06:41:53Z)
• Astrolabe: Steering Forward-Process Reinforcement Learning for Distilled Autoregressive Video Models [58.3184497327891]
  Distilled autoregressive (AR) video models enable efficient streaming generation but frequently misalign with human visual preferences.<n>We present Astrolabe, an efficient online reinforcement learning framework tailored for distilled AR models.
  arXiv  Detail & Related papers  (2026-03-17T18:32:18Z)
• Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards [69.74686029941881]
  Reinforcement Learning with Verifiable Rewards (RLVR) is an effective paradigm for improving the reasoning capabilities of large language models.<n>We propose a unified neural scheduling framework that adaptively selects high-value rollouts throughout training.<n>Experiments on six mathematical reasoning benchmarks demonstrate consistent gains in performance and training efficiency across multiple RLVR optimization methods.
  arXiv  Detail & Related papers  (2026-02-09T10:51:58Z)
• AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine Learning Engineering [52.67783579040657]
  AceGRPO is a machine learning system that prioritizes tasks at the agent's learning frontier to maximize learning efficiency.<n>Our trained Ace-30B model achieves a 100% valid submission rate on MLE-Bench-Lite, approaches the performance of proprietary frontier models, and outperforms larger open-source baselines.
  arXiv  Detail & Related papers  (2026-02-08T10:55:03Z)
• Rethinking LLM-Driven Heuristic Design: Generating Efficient and Specialized Solvers via Dynamics-Aware Optimization [21.449921296295884]
  We propose Dynamics-Aware Heuristics (DASH), a framework that co-optimizes solver search mechanisms and runtime schedules guided by a convergence-aware metric.<n>DASH improves runtime efficiency by over 3 times, while surpassing the solution quality of state-of-the-art baselines across diverse problem scales.
  arXiv  Detail & Related papers  (2026-01-14T05:06:42Z)
• Each Prompt Matters: Scaling Reinforcement Learning Without Wasting Rollouts on Hundred-Billion-Scale MoE [16.58714489761542]
  We present CompassMax-V3-Thinking, a hundred-billion-scale MoE reasoning model trained with a new RL framework built on one principle: each prompt must matter.<n>To overcome these challenges, we introduce several unified innovations.<n>The resulting model delivers strong performance across both internal and public evaluations.
  arXiv  Detail & Related papers  (2025-12-08T16:57:43Z)
• Benchmarking Generative AI Against Bayesian Optimization for Constrained Multi-Objective Inverse Design [0.15293427903448018]
  This paper investigates the performance of Large Language Models (LLMs) as generative feasibles for solving constrained multi-objective regression tasks.<n>The best-performing LLM (Math-7B) achieved a Generational Distance (GD) of 1.21, significantly outperforming the traditional BoTorch Ax baseline.<n>The findings have direct industrial applications in optimizing formulation design for resins, rheological, and chemical properties.
  arXiv  Detail & Related papers  (2025-10-29T10:37:09Z)
• OneRec-V2 Technical Report [93.91714323473678]
  OneRec reformulates recommendation as an autoregressive generation task, achieving high Model FLOPs Utilization.<n>Lazy Decoder-Only Architecture: Eliminates encoder bottlenecks, reducing total computation by 94% and training resources by 90%.<n> Preference Alignment with Real-World User Interactions: Incorporates Duration-Aware Reward Shaping and Adaptive Ratio Clipping to better align with user preferences.
  arXiv  Detail & Related papers  (2025-08-28T15:29:51Z)
• Reinforced Model Merging [53.84354455400038]
  We present an innovative framework termed Reinforced Model Merging (RMM), which encompasses an environment and agent tailored for merging tasks.<n>By utilizing data subsets during the evaluation process, we addressed the bottleneck in the reward feedback phase, thereby accelerating RMM by up to 100 times.
  arXiv  Detail & Related papers  (2025-03-27T08:52:41Z)
• Investigating Tradeoffs in Real-World Video Super-Resolution [90.81396836308085]
  Real-world video super-resolution (VSR) models are often trained with diverse degradations to improve generalizability. To alleviate the first tradeoff, we propose a degradation scheme that reduces up to 40% of training time without sacrificing performance. To facilitate fair comparisons, we propose the new VideoLQ dataset, which contains a large variety of real-world low-quality video sequences.
  arXiv  Detail & Related papers  (2021-11-24T18:58:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.