Reward Model | BestHub

JD Tech

Mar 26, 2025 · Artificial Intelligence

CTR-Driven Advertising Image Generation Using Multimodal Large Language Models (CAIG)

The JD advertising team proposes a CTR‑driven advertising image generation framework (CAIG) that leverages multimodal large language models, a novel reward model, and product‑centric preference optimization to produce ad images with superior click‑through performance, validated by extensive offline and online experiments.

CTR optimizationReward Modeladvertising image generation

0 likes · 10 min read

CTR-Driven Advertising Image Generation Using Multimodal Large Language Models (CAIG)

DaTaobao Tech

Mar 7, 2025 · Artificial Intelligence

Taobao Content AI: Summary of AIGC Content Generation and Multimodal Model Techniques

Taobao’s AIGC pipeline combines a human‑feedback multimodal reward model, audio‑visual joint pre‑training, and Mixture‑of‑Experts distillation to clean data, align outputs with user preferences, and achieve state‑of‑the‑art multimodal LLM performance that drives content cold‑start and conversion gains in e‑commerce.

AIGCContent generationData Optimization

0 likes · 10 min read

Taobao Content AI: Summary of AIGC Content Generation and Multimodal Model Techniques

JD Tech Talk

Feb 20, 2025 · Artificial Intelligence

Multi‑Agent Architecture for an E‑Commerce Business Assistant: Design, Planning, Evaluation, and Sample Generation

The document describes the evolution, design principles, key technologies, online inference workflow, evaluation methods, and sample‑generation techniques of a large‑language‑model‑based multi‑agent system that powers a 24/7 e‑commerce merchant assistant, highlighting its benefits, challenges, and future work.

AI planningLLMOnline Inference

0 likes · 21 min read

Multi‑Agent Architecture for an E‑Commerce Business Assistant: Design, Planning, Evaluation, and Sample Generation

DataFunTalk

Aug 24, 2024 · Artificial Intelligence

Improving the Mathematical Reasoning Ability of Large Language Models: Overview, Mixed Instructions, Synthetic Data, and Training Optimization

This article presents a comprehensive approach to enhancing large language models' mathematical reasoning by reviewing model architectures, introducing mixed CoT‑PoT instructions, generating and filtering synthetic data, and applying multi‑stage training optimizations such as RFT, PPO, and DPO, with detailed experimental results and Q&A.

AILarge Language ModelsReward Model

0 likes · 16 min read

Improving the Mathematical Reasoning Ability of Large Language Models: Overview, Mixed Instructions, Synthetic Data, and Training Optimization

DataFunSummit

Feb 25, 2023 · Artificial Intelligence

Understanding Reward Model Training in InstructGPT Using Ranking Sequences

This article explains how InstructGPT's reward model is trained by collecting human‑annotated ranking sequences instead of absolute scores, describes the rank‑loss formulation, provides Python code for the model and loss computation, and presents experimental results demonstrating the approach.

InstructGPTPythonRLHF

0 likes · 9 min read

Understanding Reward Model Training in InstructGPT Using Ranking Sequences