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Machine Heart
Machine Heart
Apr 8, 2026 · Artificial Intelligence

Can Generative Reasoning Re‑ranking Unlock New Gains for LLM‑Based Recommender Systems?

The article analyzes a recent paper that introduces a generative reasoning re‑ranker for LLM‑driven recommendation, detailing its SFT and RL training pipeline, semantic‑ID embedding, target vs. reject sampling strategies, and experimental gains of 2.4% Recall@5 and 1.3% NDCG@5 over the OneRec‑Think baseline.

Generative ReasoningLLMSupervised Fine‑Tuning
0 likes · 9 min read
Can Generative Reasoning Re‑ranking Unlock New Gains for LLM‑Based Recommender Systems?
Baobao Algorithm Notes
Baobao Algorithm Notes
Nov 20, 2025 · Artificial Intelligence

Why Reinforcement Learning Preserves LLM Generality Better Than Supervised Fine‑Tuning

The article analyzes why reinforcement learning (RL) fine‑tuning retains a large language model's general abilities better than supervised fine‑tuning (SFT), explaining the off‑policy distribution shift of SFT and the on‑policy data consistency, KL penalty, and trust‑region mechanisms that give RL its anti‑forgetting properties.

Catastrophic ForgettingLLMOn-Policy Data
0 likes · 8 min read
Why Reinforcement Learning Preserves LLM Generality Better Than Supervised Fine‑Tuning
DataFunSummit
DataFunSummit
Nov 3, 2025 · Artificial Intelligence

How Tencent’s LLM Powers Real‑World AI: From RAG to Agents

This article examines Tencent's large language model applications across diverse business scenarios, detailing core use cases such as content generation, intelligent customer service, and role‑playing, and explains the three key technologies—Supervised Fine‑Tuning, Retrieval‑Augmented Generation, and Agents—that enable these capabilities.

AI applicationsAgentLLM
0 likes · 4 min read
How Tencent’s LLM Powers Real‑World AI: From RAG to Agents
DataFunTalk
DataFunTalk
Oct 13, 2025 · Artificial Intelligence

How Tencent Uses RAG, GraphRAG, and Agents to Power Large Language Model Applications

This article examines Tencent's large language model deployments across diverse business scenarios, detailing core use cases such as content generation, intelligent customer service, and role‑playing, while explaining the underlying technologies of Supervised Fine‑Tuning, Retrieval‑Augmented Generation, and Agent systems.

AI applicationsAgentRAG
0 likes · 4 min read
How Tencent Uses RAG, GraphRAG, and Agents to Power Large Language Model Applications
Data Party THU
Data Party THU
Oct 1, 2025 · Artificial Intelligence

Why SFT and RL Are Two Sides of the Same Coin: A Unified Gradient Theory for LLM Post‑Training

This article analyzes a recent paper that unifies supervised fine‑tuning (SFT) and reinforcement learning (RL) for large language models under a single gradient estimator, introduces the Unified Policy Gradient Estimator (UPGE) and the Hybrid Post‑Training (HPT) algorithm, and demonstrates their superior performance on math reasoning benchmarks.

AI researchHybrid TrainingLLM
0 likes · 11 min read
Why SFT and RL Are Two Sides of the Same Coin: A Unified Gradient Theory for LLM Post‑Training
Baobao Algorithm Notes
Baobao Algorithm Notes
Aug 14, 2025 · Artificial Intelligence

Why Standard SFT Fails to Generalize and How One‑Line Dynamic Fine‑Tuning Fixes It

The article analyzes the poor generalization of supervised fine‑tuning (SFT) for large language models, reveals its gradient as a high‑variance inverse‑probability policy gradient, proposes a one‑line Dynamic Fine‑Tuning correction, and shows substantial gains on challenging math and offline RL benchmarks.

Dynamic Fine-TuningGeneralizationLLM alignment
0 likes · 7 min read
Why Standard SFT Fails to Generalize and How One‑Line Dynamic Fine‑Tuning Fixes It
Volcano Engine Developer Services
Volcano Engine Developer Services
May 22, 2025 · Artificial Intelligence

How LLMs Can Automate Ticket Escalation: Inside ByteBrain’s TickIt System

This article introduces TickIt, a ByteBrain system that leverages large language models to automatically identify and escalate critical Oncall tickets, detailing its multi‑class escalation, deduplication, and category‑guided fine‑tuning modules, experimental results, and the operational impact on cloud services.

LLMOncall analysisSupervised Fine‑Tuning
0 likes · 13 min read
How LLMs Can Automate Ticket Escalation: Inside ByteBrain’s TickIt System
AIWalker
AIWalker
May 6, 2025 · Artificial Intelligence

SimpleAR: High‑Quality 1024×1024 Images with Just 0.5B Parameters via Pretraining, SFT, and RL

SimpleAR demonstrates that a vanilla autoregressive model with only 0.5 B parameters can generate high‑fidelity 1024×1024 images, covering pretraining, supervised fine‑tuning, and reinforcement learning, achieving competitive GenEval (0.59) and DPG‑Bench (79.66) scores while reducing inference time to about 14 seconds with vLLM and KV‑cache optimizations.

BenchmarkSupervised Fine‑Tuningautoregressive
0 likes · 14 min read
SimpleAR: High‑Quality 1024×1024 Images with Just 0.5B Parameters via Pretraining, SFT, and RL
AI Algorithm Path
AI Algorithm Path
Apr 2, 2025 · Artificial Intelligence

Master the Three Essential LLM Training Stages for 2025

The article breaks down the three core stages of large‑language‑model training—pre‑training, supervised fine‑tuning, and RLHF—explaining their purpose, methods, and concrete examples while noting DeepSeek‑R1’s recent breakthrough and its implications for AI development.

AI trainingDeepSeekLLM
0 likes · 5 min read
Master the Three Essential LLM Training Stages for 2025
Architect
Architect
Mar 16, 2025 · Artificial Intelligence

Training a 0.5B LLM with Chain‑of‑Thought Reasoning: From Pre‑training to GRPO Fine‑tuning

This article walks through the complete lifecycle of building a small large‑language model, covering token‑level inference, pre‑training, post‑training steps such as supervised fine‑tuning, reward‑model creation, and reinforcement‑learning methods like DPO, PPO and GRPO, culminating in a practical 0.5B model fine‑tuned for chain‑of‑thought reasoning.

GRPOLLM trainingReward Modeling
0 likes · 22 min read
Training a 0.5B LLM with Chain‑of‑Thought Reasoning: From Pre‑training to GRPO Fine‑tuning
DataFunTalk
DataFunTalk
Mar 9, 2025 · Artificial Intelligence

Critique Fine-Tuning (CFT): Boosting Large Language Model Reasoning with Minimal Data

The paper introduces Critique Fine-Tuning (CFT), a method that replaces simple imitation in supervised fine‑tuning with critique‑based learning, achieving superior reasoning performance on mathematical benchmarks using only 50 K samples, outperforming traditional reinforcement‑learning approaches that require millions of examples.

AI reasoningCritique Fine-TuningMathematical Benchmarks
0 likes · 7 min read
Critique Fine-Tuning (CFT): Boosting Large Language Model Reasoning with Minimal Data
Architecture Digest
Architecture Digest
Feb 7, 2025 · Artificial Intelligence

Open-Source Replication of OpenAI’s o1 Model Achieves Superior Performance with Minimal Cost

A recent study by Fei‑Fei Li’s team shows that using supervised fine‑tuning on the open‑source Qwen2.5‑32B‑Instruct model can replicate and even surpass the reasoning abilities of OpenAI’s o1‑preview at a fraction of the computational cost, demonstrating a cheap yet powerful approach to large‑language‑model development.

Supervised Fine‑Tuningbudget-forcingcost-effective-ai
0 likes · 6 min read
Open-Source Replication of OpenAI’s o1 Model Achieves Superior Performance with Minimal Cost
Bilibili Tech
Bilibili Tech
Nov 5, 2024 · Artificial Intelligence

Bilibili's In-House Role-Playing Large Language Model: Architecture, Training Stages, Evaluation, and Demonstrations

Bilibili’s in‑house role‑playing large language model, built on the Index architecture and refined through pre‑training, supervised fine‑tuning, and preference optimization (PPO and DPO), achieved top scores on the Chinese CharacterEval benchmark, surpassing rivals while incorporating safety alignment and showcasing consistent, personality‑driven dialogue examples.

Content SafetySupervised Fine‑Tuningevaluation benchmark
0 likes · 13 min read
Bilibili's In-House Role-Playing Large Language Model: Architecture, Training Stages, Evaluation, and Demonstrations
NewBeeNLP
NewBeeNLP
Mar 27, 2024 · Artificial Intelligence

Deep Dive into Llama 2: Architecture, Pre‑training, SFT, and Safety Insights

This article provides a comprehensive technical overview of Meta's Llama 2 series, covering its architectural upgrades such as Group Query Attention, the pre‑training dataset and hyper‑parameters, loss behavior, benchmark comparisons, and the supervised fine‑tuning pipeline with safety considerations.

AILlama-2Model architecture
0 likes · 11 min read
Deep Dive into Llama 2: Architecture, Pre‑training, SFT, and Safety Insights
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Dec 24, 2023 · Artificial Intelligence

Llama 2: Open Foundation and Fine‑Tuned Chat Models – Overview, Training, and RLHF Details

This article provides a comprehensive English overview of Meta's Llama 2 family, describing the model sizes, pre‑training data, architectural improvements, supervised fine‑tuning, reinforcement learning with human feedback, safety evaluations, reward‑model training, and iterative optimization techniques used to produce the high‑performing Llama 2‑Chat models.

Llama-2Open‑sourceRLHF
0 likes · 33 min read
Llama 2: Open Foundation and Fine‑Tuned Chat Models – Overview, Training, and RLHF Details
DataFunSummit
DataFunSummit
Feb 10, 2023 · Artificial Intelligence

Why ChatGPT Shows Strong General Intelligence: Insights from Andrew Ng’s DeepLearning.AI Article

The article explains how techniques such as Reinforcement Learning from Human Feedback, Instruction Fine‑Tuning, Supervised Fine‑tuning and Chain‑of‑Thought contribute to ChatGPT’s impressive general‑intelligence performance, as analyzed by DeepLearning.AI founder Andrew Ng.

ChatGPTDeepLearning.AIReinforcement Learning from Human Feedback
0 likes · 2 min read
Why ChatGPT Shows Strong General Intelligence: Insights from Andrew Ng’s DeepLearning.AI Article