RLHF | BestHub

DataFunSummit

Jun 10, 2025 · Artificial Intelligence

How Quwan’s Kaitian Model Tackles Emotional AI for Social Apps – Architecture, Training Tricks, and Safety

Quwan Technology presents its Kaitian social large model, designed for personalized, emotionally rich, multimodal AI interactions, detailing its scene‑specific goals, CPT+SFT+RLHF training pipeline, data desensitization, LoRA fine‑tuning, evaluation methods, pruning, latency trade‑offs, safety mechanisms, and future feedback loops.

AI safetyLoRARLHF

0 likes · 13 min read

How Quwan’s Kaitian Model Tackles Emotional AI for Social Apps – Architecture, Training Tricks, and Safety

DaTaobao Tech

Jun 4, 2025 · Artificial Intelligence

Understanding Large Language Model Architecture, Parameters, Memory, Storage, and Fine‑Tuning Techniques

This article provides a comprehensive overview of large language models (LLMs), covering their transformer architecture, parameter counts, GPU memory and storage requirements, and detailed fine‑tuning methods such as prompt engineering, data construction, LoRA, PEFT, RLHF, and DPO, along with practical deployment and inference acceleration strategies.

DPOFine-tuningLLM

0 likes · 17 min read

Understanding Large Language Model Architecture, Parameters, Memory, Storage, and Fine‑Tuning Techniques

JD Tech

Apr 30, 2025 · Artificial Intelligence

TimeHF: A Billion‑Scale Time Series Forecasting Model Guided by Human Feedback

The JD Supply Chain algorithm team introduces TimeHF, a billion‑parameter time‑series large model that leverages RLHF to boost demand‑forecast accuracy by over 10%, detailing dataset construction, the PCTLM architecture, a custom RLHF framework (TPO), and extensive SOTA experimental results.

Big DataLarge Language ModelsRLHF

0 likes · 10 min read

TimeHF: A Billion‑Scale Time Series Forecasting Model Guided by Human Feedback

JD Tech Talk

Apr 11, 2025 · Artificial Intelligence

A Billion-Scale Pure Time Series Large Model: PCTLM with SFT and TPO for Forecasting

This article presents a pioneering billion‑parameter pure time‑series large model (PCTLM) trained on a 1.5‑billion‑sample dataset, introduces a novel RLHF framework (TPO) for time‑series forecasting, and demonstrates state‑of‑the‑art performance across multiple public benchmarks, surpassing existing models such as GPT4TS.

Big DataPCTLMRLHF

0 likes · 11 min read

A Billion-Scale Pure Time Series Large Model: PCTLM with SFT and TPO for Forecasting

DataFunSummit

Mar 30, 2025 · Artificial Intelligence

RLHF Techniques and Challenges in Large Language Models and Multimodal Applications

This article reviews reinforcement learning, RLHF, and related alignment techniques for large language models and multimodal systems, covering fundamentals, recent advances such as InstructGPT, Constitutional AI, RLAIF, Super Alignment, GPT‑4o, video LLMs, and experimental evaluations of proposed methods.

Large Language ModelsPreference LearningRLHF

0 likes · 26 min read

RLHF Techniques and Challenges in Large Language Models and Multimodal Applications

DataFunTalk

Mar 24, 2025 · Artificial Intelligence

DeepSeek R1: Open‑Source Reasoning Model and Multi‑Stage Training Insights

The interview explores DeepSeek R1's open‑source weights, its multi‑stage training pipeline—including pre‑training, supervised fine‑tuning, and RLHF—alongside innovations such as self‑consistency, chain‑of‑thought prompting, distillation, MoE architectures, and cost considerations, highlighting its impact on the future of large language models.

AI trainingChain-of-ThoughtDeepSeek

0 likes · 20 min read

DeepSeek R1: Open‑Source Reasoning Model and Multi‑Stage Training Insights

Code Mala Tang

Mar 1, 2025 · Artificial Intelligence

Why Do Large Language Models Hallucinate and How Can We Fix It?

This article explains why large language models produce plausible‑looking but false information, traces the problem to the supervised fine‑tuning stage, and outlines mitigation techniques such as knowledge interrogation, RLHF, and tool‑augmented search to reduce hallucinations.

LLMRLHFWeb Search

0 likes · 12 min read

Why Do Large Language Models Hallucinate and How Can We Fix It?

Tencent Technical Engineering

Feb 24, 2025 · Artificial Intelligence

Understanding GRPO: Group Relative Policy Optimization in Reinforcement Learning and Large Language Models

The article reviews reinforcement-learning fundamentals and the progression from policy-gradient to PPO, then introduces Group Relative Policy Optimization (GRPO)—a critic-free method that normalizes rewards across multiple sampled outputs to compute group-relative advantages—and shows how DeepSeek-R1 leverages GRPO with rule-based rewards to achieve strong reasoning performance.

GRPOPPORLHF

0 likes · 16 min read

Understanding GRPO: Group Relative Policy Optimization in Reinforcement Learning and Large Language Models

DevOps

Feb 23, 2025 · Artificial Intelligence

Understanding Reinforcement Learning, RLHF, PPO and GRPO for AI Applications

This article explains how DeepSeek‑R1‑Zero uses group‑relative policy optimization (GRPO) to enhance inference without labeled data, introduces reinforcement learning with human feedback (RLHF) and its components, and compares the PPO and GRPO algorithms, highlighting their suitable engineering scenarios and practical implications for AI applications.

AI model trainingGRPOPPO

0 likes · 15 min read

Understanding Reinforcement Learning, RLHF, PPO and GRPO for AI Applications

Top Architect

Feb 9, 2025 · Artificial Intelligence

DeepSeek‑R1: Training Pipeline, Reinforcement‑Learning Techniques, and Experimental Results

The article reviews DeepSeek‑R1’s training methodology—including cold‑start data collection, multi‑stage RL fine‑tuning, SFT data generation, and model distillation—highlights its performance comparable to OpenAI‑o1‑1217, and discusses key contributions, reward design, successful experiments, and failed attempts.

AI researchDeepSeekLLM

0 likes · 12 min read

DeepSeek‑R1: Training Pipeline, Reinforcement‑Learning Techniques, and Experimental Results

Architect

Feb 6, 2025 · Artificial Intelligence

DeepSeek‑R1: Reinforcement‑Learning‑Driven Long‑Chain Reasoning for Large Language Models

The article reviews DeepSeek‑R1, detailing its reinforcement‑learning‑based training pipeline that uses minimal supervised data, cold‑start fine‑tuning, multi‑stage RL, rejection‑sampling SFT, and distillation to achieve reasoning performance comparable to OpenAI‑o1‑1217, while also discussing successful contributions and failed experiments.

AI researchDeepSeek-R1LLM reasoning

0 likes · 11 min read

DeepSeek‑R1: Reinforcement‑Learning‑Driven Long‑Chain Reasoning for Large Language Models

DataFunSummit

Jan 21, 2025 · Artificial Intelligence

NVIDIA NeMo Full Stack: End‑to‑End Large Language Model Training, Alignment, and RLHF

This article presents NVIDIA's NeMo technology stack for end‑to‑end large language model (LLM) training, covering the full software pipeline, model alignment with reinforcement learning from human feedback (RLHF), performance optimizations such as model parallelism, FP8, TensorRT‑LLM inference, dynamic load balancing, and future research directions.

GPU optimizationLLMNeMo

0 likes · 24 min read

NVIDIA NeMo Full Stack: End‑to‑End Large Language Model Training, Alignment, and RLHF

Xiaohongshu Tech REDtech

Jan 2, 2025 · Artificial Intelligence

Xiaohongshu's Self-developed RLHF System for Multimodal Large Language Models: Design, Optimization, and Performance

Xiaohongshu’s team unveiled a self‑developed RLHF system that trains multimodal large language models using heterogeneous and homogeneous network architectures, extensive PPO optimizations, and Medusa speculative sampling, achieving over 50% throughput gains, reduced hardware needs, and 5‑20% performance improvements on zero‑shot benchmarks.

MedusaPPOPRM

0 likes · 21 min read

Xiaohongshu's Self-developed RLHF System for Multimodal Large Language Models: Design, Optimization, and Performance

php中文网 Courses

Dec 13, 2024 · Artificial Intelligence

OpenAI Day 2: Launch of Reinforcement Learning from Human Feedback (RLHF) Model for Enhanced AI Capabilities

OpenAI announced on the second day of its twelve‑day event that it has integrated Reinforcement Learning from Human Feedback (RLHF) into its 001 series models, demonstrating significant reasoning improvements, showcasing legal and medical use cases, and promising a public release early next year.

AI Model Fine-tuningOpenAIRLHF

0 likes · 5 min read

OpenAI Day 2: Launch of Reinforcement Learning from Human Feedback (RLHF) Model for Enhanced AI Capabilities

DataFunTalk

Sep 23, 2024 · Artificial Intelligence

Comprehensive Guide to Selecting, Adapting, and Deploying Large Language Models for Enterprise Applications

This article provides an in‑depth, step‑by‑step guide on how enterprises can choose between open‑source and closed‑source large language models, adapt them through incremental pre‑training, instruction fine‑tuning, and reinforcement learning, and finally deploy them across front‑office, middle‑office, and back‑office scenarios to drive digital transformation.

Large Language ModelsRLHFRetrieval-Augmented Generation

0 likes · 28 min read

Comprehensive Guide to Selecting, Adapting, and Deploying Large Language Models for Enterprise Applications

Baidu Geek Talk

Aug 26, 2024 · Artificial Intelligence

RLHF Performance Optimization: PPO Algorithm Acceleration Techniques

The article presents three RLHF‑PPO acceleration techniques—TRT‑LLM‑based text generation speedups, selective activation recomputation with sequence parallelism for dynamic memory reduction, and overlapping pipeline stages for system‑level parallelism—demonstrating a 350 % throughput boost on a 10 B model using 16 A100 GPUs.

GPU optimizationLarge Language ModelsPPO optimization

0 likes · 16 min read

RLHF Performance Optimization: PPO Algorithm Acceleration Techniques

Tencent Advertising Technology

Aug 15, 2024 · Artificial Intelligence

Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding

This paper introduces RLLR, a label‑sensitive reward reinforcement learning method that improves natural language understanding tasks by aligning training objectives with label accuracy, and demonstrates its effectiveness across eight public NLU datasets and real‑world advertising feature evaluation, outperforming standard RLHF and SFT baselines.

Natural Language UnderstandingRLHFadvertising

0 likes · 14 min read

Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding

DataFunSummit

Aug 8, 2024 · Artificial Intelligence

Exploring Training and Alignment Techniques for Financial Large Models

The announcement details a DataFun Summit 2024 session where Du Xiaoman AI researcher Huo Liangyu will present on the challenges, development, and alignment methods of the Xuan Yuan financial large language model, highlighting RLHF techniques, data collection, and real‑world deployment insights for the finance sector.

AIFinanceFinancial AI

0 likes · 6 min read

Exploring Training and Alignment Techniques for Financial Large Models

Kuaishou Tech

Jul 18, 2024 · Artificial Intelligence

Multidimensional Preference Model (MPS) for Text-to-Image Generation: Dataset, Architecture, and Experimental Analysis

This article introduces the Multidimensional Preference Model (MPS), the first multi‑dimensional scoring system for evaluating text‑to‑image generation, built on the newly released MHP dataset with extensive human annotations across aesthetic, semantic alignment, detail quality, and overall preference dimensions, and demonstrates its superior performance through comprehensive experiments and RLHF integration.

AI evaluationMHP datasetMPS

0 likes · 10 min read

Multidimensional Preference Model (MPS) for Text-to-Image Generation: Dataset, Architecture, and Experimental Analysis

Rare Earth Juejin Tech Community

May 1, 2024 · Artificial Intelligence

Hyper‑SD: Trajectory‑Segmented Consistency Model for Accelerating Diffusion Image Generation

Hyper‑SD introduces a trajectory‑segmented consistency distillation framework that combines trajectory‑preserving and trajectory‑reconstruction strategies, integrates human‑feedback learning and score distillation, and achieves state‑of‑the‑art low‑step image generation performance on both SD1.5 and SDXL models.

AI accelerationModel DistillationRLHF

0 likes · 10 min read

Hyper‑SD: Trajectory‑Segmented Consistency Model for Accelerating Diffusion Image Generation