mlsanityData to 14 Oct 2026

TaoSR1: The Thinking Model for E-commerce Relevance Search

arXiv 2508.12365 ↗First posted 17 Aug 2025cs.IR cs.AI cs.CLcited by 10

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

Query-product relevance prediction is a core task in e-commerce search. BERT-based models excel at semantic matching but lack complex reasoning capabilities. While Large Language Models (LLMs) are explored, most still use discriminative fine-tuning or distill to smaller models for deployment. We propose a framework to directly deploy LLMs for this task, addressing key challenges: Chain-of-Thought (CoT) error accumulation, discriminative hallucination, and deployment feasibility. Our framework, TaoSR1, involves three stages: (1) Supervised Fine-Tuning (SFT) with CoT to instill reasoning; (2) Offline sampling with a pass@N strategy and Direct Preference Optimization (DPO) to improve generation quality; and (3) Difficulty-based dynamic sampling with Group Relative Policy Optimization (GRPO) to mitigate discriminative hallucination. Additionally, post-CoT processing and a cumulative probability-based partitioning method enable efficient online deployment. TaoSR1 significantly outperforms baselines on offline datasets and achieves substantial gains in online side-by-side human evaluations, introducing a novel paradigm for applying CoT reasoning to relevance classification.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 16 of its 33 references that are in the map. First posted on arXiv 17 Aug 2025.

Builds on

16 of 33 references.

References · 16

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  3. 2025Qwen3 Technical Reportcited by 8,820
  4. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  5. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,810
  6. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,986
  7. 2024OpenAI o1 System Cardcited by 1,244
  8. 2025Group Sequence Policy Optimizationcited by 835
  9. 2025Kimi K2: Open Agentic Intelligencecited by 567
  10. 2025Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learningcited by 161
  11. 2025Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Librarycited by 101
  12. 2025Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-Tuningcited by 44
4 more
  1. 2024Large Language Models for Relevance Judgment in Product Searchcited by 8
  2. 2025GenCLS++: Pushing the Boundaries of Generative Classification in LLMs Through Comprehensive SFT and RL Studies Across Diverse Datasetscited by 6
  3. 2024Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Promptingcited by 5
  4. 2021Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigmcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 7.

10 papers.

  1. 2025Large Reasoning Embedding Models: Towards Next-Generation Dense Retrieval Paradigmcited by 9
  2. 2025Retrieval-GRPO: A Multi-Objective Reinforcement Learning Framework for Dense Retrieval in Taobao Searchcited by 4
  3. 2026Thinking Broad, Acting Fast: Latent Reasoning Distillation from Multi-Perspective Chain-of-Thought for E-Commerce Relevancecited by 2
  4. 2025LORE: A Large Generative Model for Search Relevancecited by 1
  5. 2026K-CARE: Knowledge-driven Symmetrical Contextual Anchoring and Analogical Prototype Reasoning for E-commerce Relevancecited by 1
  6. 2026Valley3: Scaling Omni Foundation Models for E-commercecited by 1
  7. 2026Sandwich Reasoning: An Answer-Reasoning-Answer Approach for Low-Latency Query Correctioncited by 0
  8. 2026RAG-Match: Retrieval-Augmented Knowledge Injection and Hierarchical Reasoning for Calibrated Semantic Relevancecited by 0
  9. 2026Graph-GRPO: Dependency-Aware Credit Assignment for Generative E-commerce Search Relevancecited by 0
  10. 2026SSR-GRPO: Integrating Supervision and Semantic IDs into Reinforcement Learning for Dense Retrieval in E-commercecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Automated Query-Product Relevance Labeling using Large Language Models for E-commerce Search
  2. 2025LREF: A Novel LLM-based Relevance Framework for E-commerce
  3. 2025TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance
  4. 2025From Reasoning LLMs to BERT: A Two-Stage Distillation Framework for Search Relevance
  5. 2026CORE: A Unified Cascaded Ordinal Relevance Estimation Framework for E-commerce Search ↗
  6. 2025A Technical Report on the Second Place Solution for the CIKM 2025 AnalytiCup Competition ↗
  7. 2025Optimizing Generative Ranking Relevance via Reinforcement Learning in Xiaohongshu Search
  8. 2026Semantic Retrieval for Product Search in E-Commerce