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822 articles · Page 6 of 9
Mafengwo Technology
Mafengwo Technology
Apr 30, 2025 · Artificial Intelligence

How MaFengWo’s mfw-32B Travel LLM Outperforms DeepSeek‑R1 in Speed and Accuracy

The article details the development, training, and evaluation of MaFengWo's 32‑billion‑parameter travel large language model (mfw‑32B), highlighting its superior itinerary planning, personalized demand capture, budget management, and resource efficiency compared to DeepSeek‑R1, and describing the SFT and reinforcement‑learning stages that enabled these gains.

LoRAModel Evaluationai-optimization
0 likes · 14 min read
How MaFengWo’s mfw-32B Travel LLM Outperforms DeepSeek‑R1 in Speed and Accuracy
AIWalker
AIWalker
Apr 28, 2025 · Artificial Intelligence

SimpleAR: Autoregressive Visual Generation at 1024×1024 Using Only 0.5B Parameters

SimpleAR is a minimalist autoregressive visual generation framework that, with only 0.5 B parameters, achieves competitive 1024×1024 image synthesis through a three‑stage pipeline of large‑scale pretraining, supervised fine‑tuning, and GRPO‑based reinforcement learning, and demonstrates significant inference speedups using KV‑cache, vLLM, and speculative decoding.

Inference Accelerationautoregressive generationbenchmark
0 likes · 14 min read
SimpleAR: Autoregressive Visual Generation at 1024×1024 Using Only 0.5B Parameters
DataFunTalk
DataFunTalk
Apr 25, 2025 · Artificial Intelligence

Does Reinforcement Learning Really Expand Reasoning Capacity in Large Language Models? Insights from Recent Empirical Study

Recent empirical research by Tsinghua’s LeapLab and Shanghai Jiao Tong University reveals that reinforcement‑learning‑based fine‑tuning (RLVR) improves sampling efficiency but does not extend the fundamental reasoning abilities of large language models beyond their base capabilities, as demonstrated across mathematics, code, and visual reasoning benchmarks.

AI researchLarge Language ModelsRLVR
0 likes · 12 min read
Does Reinforcement Learning Really Expand Reasoning Capacity in Large Language Models? Insights from Recent Empirical Study
AntTech
AntTech
Apr 24, 2025 · Artificial Intelligence

Key Takeaways from Ant Group and Tsinghua’s Presentations on the AReaL Reinforcement Learning Framework and AWorld Multi‑Agent Framework at ICLR 2025

At ICLR 2025 in Singapore, Ant Group and Tsinghua University showcased the open‑source reinforcement‑learning platform AReaL and the multi‑agent system AWorld, highlighting their recent breakthroughs, system design challenges, performance results on the GAIA benchmark, and upcoming development plans.

AI frameworksICLR2025Open Source
0 likes · 7 min read
Key Takeaways from Ant Group and Tsinghua’s Presentations on the AReaL Reinforcement Learning Framework and AWorld Multi‑Agent Framework at ICLR 2025
Kuaishou Tech
Kuaishou Tech
Apr 24, 2025 · Artificial Intelligence

Two‑Stage History‑Resampling Policy Optimization (SRPO) for Large‑Scale LLM Reinforcement Learning

The article introduces SRPO, a two‑stage history‑resampling reinforcement‑learning framework that systematically tackles common GRPO training issues and achieves state‑of‑the‑art performance on both math and code benchmarks with far fewer training steps, while also revealing emergent self‑reflection behaviors in large language models.

LLM OptimizationSRPOcross-domain training
0 likes · 12 min read
Two‑Stage History‑Resampling Policy Optimization (SRPO) for Large‑Scale LLM Reinforcement Learning
AI Frontier Lectures
AI Frontier Lectures
Apr 24, 2025 · Artificial Intelligence

How d1 Boosts Reasoning in Diffusion LLMs with Reinforcement Learning

Researchers from UCLA and Meta AI introduce d1, a two‑stage post‑training framework that combines supervised fine‑tuning and a novel diffu‑GRPO reinforcement‑learning algorithm to enable efficient reasoning in masked diffusion large language models, achieving state‑of‑the‑art performance on multiple math and logic benchmarks.

AIDiffusion LLMd1
0 likes · 9 min read
How d1 Boosts Reasoning in Diffusion LLMs with Reinforcement Learning
AI Frontier Lectures
AI Frontier Lectures
Apr 24, 2025 · Artificial Intelligence

Why AI’s Second Half Is About Products, Not Just Models – A Deep Dive

The article argues that AI is entering a new phase where defining real‑world tasks and robust evaluation outweigh pure model improvements, highlighting the rise of reasoning‑augmented reinforcement learning, the need for product‑oriented thinking, and the shortcomings of current i.i.d. benchmark practices.

AI trendsindustry insightproduct focus
0 likes · 9 min read
Why AI’s Second Half Is About Products, Not Just Models – A Deep Dive
AntTech
AntTech
Apr 21, 2025 · Artificial Intelligence

InclusionAI Community to Present AReaL Reinforcement Learning Framework and AWorld Multi‑Agent Framework at ICLR 2025

The InclusionAI open‑source community, initiated by Ant Group, will showcase the latest advances of its reinforcement‑learning framework AReaL and multi‑agent framework AWorld at the ICLR 2025 conference in Singapore, highlighting performance breakthroughs, open‑source contributions, and industry‑focused AI research.

AReaLAWorldAnt Group
0 likes · 5 min read
InclusionAI Community to Present AReaL Reinforcement Learning Framework and AWorld Multi‑Agent Framework at ICLR 2025
AI Algorithm Path
AI Algorithm Path
Apr 20, 2025 · Artificial Intelligence

Boosting Visual Reasoning in VLMs with Reinforcement Learning

The article analyzes how reinforcement learning, which transformed LLM reasoning in DeepSeek, can be applied to visual‑language models to overcome the limitations of traditional chain‑of‑thought prompting and supervised fine‑tuning, presenting concrete reward designs, training pipelines, and a critical assessment of their strengths and weaknesses.

LLMRL Trainingchain-of-thought
0 likes · 10 min read
Boosting Visual Reasoning in VLMs with Reinforcement Learning
Fighter's World
Fighter's World
Apr 18, 2025 · Artificial Intelligence

Rethinking the AGI Roadmap: From Data Imitation to Experience‑Driven Superiority

The article analyzes the emerging "Era of Experience" in AI, arguing that reliance on static human data limits progress and that reinforcement learning‑based experiential learning—exemplified by AlphaZero—offers a path toward surpassing human knowledge, while outlining the technical, safety, and ethical challenges ahead.

AGIAlphaZeroArtificial Intelligence
0 likes · 19 min read
Rethinking the AGI Roadmap: From Data Imitation to Experience‑Driven Superiority
AI Frontier Lectures
AI Frontier Lectures
Apr 17, 2025 · Artificial Intelligence

Why Reinforcement Learning Fails to Boost Small LLM Reasoning: A Deep Dive

This article analyzes a recent study on language‑model reasoning, revealing that reinforcement learning often brings little or no improvement, while evaluation variance caused by seeds, hardware, and decoding settings can dramatically affect benchmark results, and supervised fine‑tuning emerges as a more reliable path.

LLMreinforcement learningreproducibility
0 likes · 12 min read
Why Reinforcement Learning Fails to Boost Small LLM Reasoning: A Deep Dive
Data Thinking Notes
Data Thinking Notes
Apr 15, 2025 · Artificial Intelligence

Understanding AI Agents: From Reinforcement Learning to LLM-Powered Planning

Professor Li Hongyi’s lecture provides a comprehensive, step‑by‑step exploration of AI agents, covering their definitions, reinforcement‑learning roots, LLM integration, memory mechanisms, tool usage, planning strategies, benchmarks, and practical examples, offering a valuable resource for anyone studying modern artificial intelligence.

AI agentsLarge Language ModelsMemory
0 likes · 67 min read
Understanding AI Agents: From Reinforcement Learning to LLM-Powered Planning
Volcano Engine Developer Services
Volcano Engine Developer Services
Apr 14, 2025 · Artificial Intelligence

Introducing Multi‑SWE‑bench: The First Multilingual Code‑Fix Benchmark for LLMs

ByteDance’s Doubao model team has open‑sourced Multi‑SWE‑bench, a multilingual benchmark covering seven major programming languages with 1,632 real‑world bug‑fix tasks, complete Docker environments, difficulty grading, and strict human validation, aiming to evaluate and advance large‑language‑model code‑repair capabilities beyond Python.

LLM BenchmarkSoftware Engineeringcode repair
0 likes · 11 min read
Introducing Multi‑SWE‑bench: The First Multilingual Code‑Fix Benchmark for LLMs
AI Algorithm Path
AI Algorithm Path
Apr 13, 2025 · Artificial Intelligence

Understanding GRPO: Group Relative Policy Optimization for LLM Training

The article explains GRPO, a reinforcement‑learning algorithm that extends PPO with group sampling, no value network, dual penalties and KL regularisation, showing how it improves efficiency and stability when fine‑tuning large language models such as DeepSeek‑Math and DeepSeek‑R1.

DeepSeekGRPOLarge Language Models
0 likes · 6 min read
Understanding GRPO: Group Relative Policy Optimization for LLM Training
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Apr 9, 2025 · Artificial Intelligence

Why Scaling Laws Fail for Video MLLMs: Uncovering the Temporal Hacking Problem

The article analyzes the anti‑scaling phenomenon in video large‑language models, identifies a “temporal hacking” shortcut where models focus on a few key frames, formalizes it via reward‑hacking theory, introduces the Temporal Perplexity (TPL) metric, and proposes an Unhackable Temporal Rewarding (UTR) framework to mitigate the issue.

Temporal PerplexityUTRreinforcement learning
0 likes · 14 min read
Why Scaling Laws Fail for Video MLLMs: Uncovering the Temporal Hacking Problem
AI Algorithm Path
AI Algorithm Path
Apr 2, 2025 · Artificial Intelligence

Vision‑Reasoning Model: Enabling LLMs to See and Think

The article analyzes the limitations of current visual language models and large reasoning models, proposes a combined Vision‑Reasoning Model (VRM), details its architecture using LLaVA, describes end‑to‑end fine‑tuning and reinforcement‑learning reward design, and argues that such models will become the next breakthrough in AI.

DeepSeekLLaVAVisual Reasoning
0 likes · 9 min read
Vision‑Reasoning Model: Enabling LLMs to See and Think
Subtle Storm
Subtle Storm
Apr 2, 2025 · Artificial Intelligence

Deep Dive into Machine Learning for Large Language Models

This article explains how machine learning powers large language models, covering fundamental concepts, classifications, core principles of supervised, unsupervised and reinforcement learning, typical algorithms—including linear models, tree models, neural networks, and Transformers—and the full training pipeline from data preprocessing to RLHF, followed by real‑world application examples.

Fine-tuningRLHFSupervised Learning
0 likes · 8 min read
Deep Dive into Machine Learning for Large Language Models
Data Thinking Notes
Data Thinking Notes
Mar 30, 2025 · Artificial Intelligence

How DeepSeek‑R1 and Kimi‑K1.5 Push the Boundaries of Strong Reasoning Models

This comprehensive analysis by the Peking University AI Alignment team dissects the technical innovations behind DeepSeek‑R1, DeepSeek‑R1 Zero, and Kimi‑K1.5, covering reinforcement‑learning‑based post‑training, rule‑based rewards, GRPO optimization, scaling laws, multimodal extensions, safety challenges, and future research directions.

AI AlignmentDeepSeekKimi
0 likes · 57 min read
How DeepSeek‑R1 and Kimi‑K1.5 Push the Boundaries of Strong Reasoning Models
Fighter's World
Fighter's World
Mar 29, 2025 · Industry Insights

A Year in AI: Key Insights from the Unsupervised Learning & Latent Space Podcast

The podcast recap dissects a year of rapid AI change, highlighting surprise‑fast open‑source model releases, shifting foundation‑model dynamics, the rise of GPT wrappers, over‑hyped agents, undervalued memory, product‑market fit debates, infrastructure opportunities, and lingering mysteries like RL in non‑verifiable domains.

AI infrastructureAI trendsAgents
0 likes · 22 min read
A Year in AI: Key Insights from the Unsupervised Learning & Latent Space Podcast
JavaEdge
JavaEdge
Mar 27, 2025 · Artificial Intelligence

Can a Single LLM Both See and Reason? Exploring Visual Reasoning Models (VRM)

This article examines the limitations of current vision‑language and reasoning models, proposes a visual reasoning model (VRM) that can process images and perform deep logical inference, and discusses architecture, training methods, reinforcement‑learning reward designs, and practical challenges.

Artificial IntelligenceLLMVisual Reasoning
0 likes · 8 min read
Can a Single LLM Both See and Reason? Exploring Visual Reasoning Models (VRM)
JD Tech
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 optimizationMultimodal LLMReward Model
0 likes · 10 min read
CTR-Driven Advertising Image Generation Using Multimodal Large Language Models (CAIG)
AI Frontier Lectures
AI Frontier Lectures
Mar 24, 2025 · Artificial Intelligence

What Can AI Agents Learn from the Latest AIR 2025 Research?

The article compiles insights from the AIR 2025 conference and related talks, covering the evolution of agents from reinforcement‑learning to LLM‑driven systems, novel agent architectures like AIDE, GUI agents, natural‑language reinforcement learning, and scaling advances in large language models such as Qwen, while highlighting key algorithms, benchmarks, and open research questions.

AI agentsAgent ArchitectureGUI agents
0 likes · 27 min read
What Can AI Agents Learn from the Latest AIR 2025 Research?
JD Tech Talk
JD Tech Talk
Mar 24, 2025 · Artificial Intelligence

MaRCA: Multi‑Agent Reinforcement Learning Computation Allocation for Full‑Chain Ad Serving

This article presents MaRCA, a multi‑agent reinforcement learning framework that allocates computation resources across the full ad‑serving chain by modeling user value, compute consumption, and action rewards, enabling fine‑grained power‑tilting toward high‑quality traffic and achieving significant business gains under strict latency constraints.

Load Balancingad servingai-optimization
0 likes · 16 min read
MaRCA: Multi‑Agent Reinforcement Learning Computation Allocation for Full‑Chain Ad Serving
Architect
Architect
Mar 23, 2025 · Artificial Intelligence

The Future of AI Agents: From Prompt‑Driven Workflows to Model‑as‑Product and Reinforcement‑Learning‑Powered Agents

The article argues that the next wave of AI agents will shift from brittle, prompt‑driven workflows like Manus to truly autonomous, model‑centric agents trained with reinforcement learning and reasoning, exemplified by OpenAI's DeepResearch and Anthropic's Claude Sonnet 3.7, while the API‑driven market model collapses.

AI agentsClaudeDeepResearch
0 likes · 28 min read
The Future of AI Agents: From Prompt‑Driven Workflows to Model‑as‑Product and Reinforcement‑Learning‑Powered Agents
Baobao Algorithm Notes
Baobao Algorithm Notes
Mar 20, 2025 · Artificial Intelligence

Unlocking Large‑Scale Deep Reinforcement Learning: PPO, GAE, and PPG Deep Dive

This comprehensive guide examines large‑scale deep reinforcement learning, detailing policy‑gradient fundamentals, the mathematics of PPO and GAE, practical implementation tricks, reward and observation normalization, network initialization, and the newer Phasic Policy Gradient method, all supported by code snippets and key research references.

Deep RLGAEPPG
0 likes · 19 min read
Unlocking Large‑Scale Deep Reinforcement Learning: PPO, GAE, and PPG Deep Dive
Baobao Algorithm Notes
Baobao Algorithm Notes
Mar 19, 2025 · Artificial Intelligence

Why Does GRPO Loss Start at Zero and Grow During OpenR1 Training?

The article explains why the GRPO loss in OpenR1 and trl starts at zero and then rises, detailing the underlying KL‑divergence formulation, the single‑step update mechanism, and how gradients are preserved despite a zero scalar loss, with code examples from the trl implementation.

GRPOLoss InitializationOpenR1
0 likes · 5 min read
Why Does GRPO Loss Start at Zero and Grow During OpenR1 Training?
JD Retail Technology
JD Retail Technology
Mar 18, 2025 · Artificial Intelligence

Multi‑Agent Reinforcement Learning Based Full‑Chain Computation Allocation (MaRCA) for Advertising Systems

MaRCA, a multi‑agent reinforcement‑learning framework, allocates compute across JD’s advertising playback chain by jointly estimating user value, resource consumption, and action outcomes while dynamically adjusting to real‑time load, achieving roughly 15 % higher ad revenue without extra compute resources.

AdvertisingCompute Schedulingdeep learning
0 likes · 18 min read
Multi‑Agent Reinforcement Learning Based Full‑Chain Computation Allocation (MaRCA) for Advertising Systems
Data Thinking Notes
Data Thinking Notes
Mar 16, 2025 · Artificial Intelligence

Why DeepSeek R1 Swaps PPO for GRPO: A Deep Dive into RLHF Alternatives

DeepSeek‑R1 replaces the traditional PPO‑based RLHF approach with GRPO, reducing reliance on human‑labeled data by using pure reinforcement learning environments and carefully designed reward mechanisms; the article explains reinforcement learning fundamentals, compares PPO, DPO and GRPO, and offers practical application recommendations.

AI AlignmentDPOGRPO
0 likes · 14 min read
Why DeepSeek R1 Swaps PPO for GRPO: A Deep Dive into RLHF Alternatives
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 trainingSupervised Fine‑Tuning
0 likes · 22 min read
Training a 0.5B LLM with Chain‑of‑Thought Reasoning: From Pre‑training to GRPO Fine‑tuning
AI Algorithm Path
AI Algorithm Path
Mar 14, 2025 · Artificial Intelligence

Understanding Different Types of AI Agents: From Simple Reflex to Multi‑Agent Systems

This article introduces the main categories of AI agents—including simple reflex, model‑based, goal‑based, utility‑based, learning, hierarchical, and multi‑agent systems—explaining their operating principles, typical use cases, advantages, limitations, and providing concrete Python code examples for each.

AI agentsAgent TypesPython
0 likes · 19 min read
Understanding Different Types of AI Agents: From Simple Reflex to Multi‑Agent Systems
JD Retail Technology
JD Retail Technology
Mar 14, 2025 · Artificial Intelligence

CTR-Driven Advertising Image Generation Using Multimodal Large Language Models

The paper presents CAIG, a CTR‑driven advertising image generation pipeline that pre‑trains a multimodal LLM on e‑commerce data, trains a reward model on CTR‑labeled image pairs, and fine‑tunes generation via product‑centric preference optimization, achieving state‑of‑the‑art online and offline performance.

AICTRMultimodal LLM
0 likes · 11 min read
CTR-Driven Advertising Image Generation Using Multimodal Large Language Models
JD Tech Talk
JD Tech Talk
Mar 13, 2025 · Artificial Intelligence

CTR-Driven Advertising Image Generation with Multimodal Large Language Models

This paper proposes CAIG, a novel method for generating high-CTR advertising images using multimodal large language models, combining reinforcement learning and preference optimization to align generated content with product features.

CTR predictionadvertising image generationmultimodal large language models
0 likes · 10 min read
CTR-Driven Advertising Image Generation with Multimodal Large Language Models
NewBeeNLP
NewBeeNLP
Mar 11, 2025 · Artificial Intelligence

How DeepSeek’s New Architecture Redefines LLM Efficiency and Performance

This article analyzes DeepSeek’s recent breakthroughs—including the Multi‑Head Latent Attention (MLA), Group Relative Policy Optimization (GRPO), and a refined Mixture‑of‑Experts design—along with its three‑stage training pipeline, RL‑only R1‑Zero variant, and benchmark comparisons against GPT‑4o‑Mini and Llama 3.1, highlighting both gains and remaining challenges.

DeepSeekLLMMixture of Experts
0 likes · 18 min read
How DeepSeek’s New Architecture Redefines LLM Efficiency and Performance
Architect
Architect
Mar 9, 2025 · Artificial Intelligence

Experiments with Reinforcement Learning Fine‑Tuning of a 0.5B Qwen Model on the KK Dataset

The author reports a series of reinforcement‑learning‑based fine‑tuning experiments on a 0.5‑billion‑parameter Qwen‑0.5VB instruct model using the KK dataset, detailing reward design adjustments, curriculum‑style data scaling, observed convergence issues, and hypotheses about why small models fail to develop long reasoning chains.

LLM fine-tuningReward Designcurriculum learning
0 likes · 11 min read
Experiments with Reinforcement Learning Fine‑Tuning of a 0.5B Qwen Model on the KK Dataset
Top Architect
Top Architect
Mar 9, 2025 · Artificial Intelligence

Alibaba Unveils Qwen QwQ-32B: A Compact Open‑Source LLM Rivaling DeepSeek

Alibaba has released the open‑source Qwen QwQ‑32B model, a 32‑billion‑parameter LLM that matches DeepSeek‑R1's performance while being deployable on consumer‑grade GPUs, and the announcement is accompanied by extensive promotional offers for AI‑related products and services.

AI BenchmarkAlibabaOpen Source
0 likes · 7 min read
Alibaba Unveils Qwen QwQ-32B: A Compact Open‑Source LLM Rivaling DeepSeek
Baobao Algorithm Notes
Baobao Algorithm Notes
Mar 6, 2025 · Artificial Intelligence

Alibaba Unveils QwQ-32B: A 32‑Billion‑Parameter Inference Model with Agent Capabilities

Alibaba has open‑sourced its new QwQ‑32B inference model, a 32.5‑billion‑parameter transformer that rivals top models like DeepSeek‑R1 and o1‑mini, features integrated agent abilities for tool use and critical thinking, and offers a low inference barrier with extensive technical specifications and RL‑based training details.

AlibabaTransformeragent capabilities
0 likes · 4 min read
Alibaba Unveils QwQ-32B: A 32‑Billion‑Parameter Inference Model with Agent Capabilities
Subtle Storm
Subtle Storm
Mar 5, 2025 · Artificial Intelligence

What Makes DeepSeek’s Large Model Technically Advanced?

DeepSeek’s large model combines a Transformer‑based architecture, Mixture of Experts, multi‑head latent attention, massive multimodal pre‑training, model compression techniques, reinforcement learning, open APIs, and ethical safeguards, delivering high‑performance, scalable AI across language, vision, and decision‑making tasks.

DeepSeekLLMMixture of Experts
0 likes · 12 min read
What Makes DeepSeek’s Large Model Technically Advanced?
Ma Wei Says
Ma Wei Says
Mar 4, 2025 · Artificial Intelligence

Microsoft’s Open‑Source Multimodal AI Agent Model Magma: Capabilities and Innovations

On February 25 2025, Microsoft open‑sourced its first multimodal AI agent foundation model, Magma, which extends multimodal processing to images, video, and text, introduces Set‑of‑Mark and Trace‑of‑Mark techniques for spatial‑temporal reasoning, optimizes modular inference for edge devices, and integrates reinforcement learning for adaptive task execution.

Edge computingFoundation ModelMagma
0 likes · 6 min read
Microsoft’s Open‑Source Multimodal AI Agent Model Magma: Capabilities and Innovations
Architect
Architect
Mar 3, 2025 · Artificial Intelligence

Unlocking Reasoning LLMs: Methods, DeepSeek R1 Insights, and Cost‑Effective Strategies

This article examines how to build and improve reasoning‑capable large language models, explains the definition and use‑cases of reasoning models, details DeepSeek‑R1’s training pipeline, compares four key enhancement methods—including inference‑time scaling, pure RL, SFT + RL, and distillation—and offers budget‑friendly advice.

AI researchDeepSeekInference Scaling
0 likes · 27 min read
Unlocking Reasoning LLMs: Methods, DeepSeek R1 Insights, and Cost‑Effective Strategies
Subtle Storm
Subtle Storm
Mar 3, 2025 · Artificial Intelligence

Everything You Need to Know About AI Agents (Intelligent Agents)

This article explains AI agents—also called intelligent agents—their definition, core concepts, components, classifications, key characteristics, underlying technologies, real‑world applications, and future research directions such as AGI, human‑machine collaboration, and edge deployment.

AGIAI AgentEdge computing
0 likes · 9 min read
Everything You Need to Know About AI Agents (Intelligent Agents)
Architect
Architect
Feb 27, 2025 · Artificial Intelligence

Understanding Inference Large Language Models: DeepSeek‑R1 and the Rise of Test‑Time Computation

This article explains how inference‑oriented large language models such as DeepSeek‑R1 and OpenAI o1‑mini shift AI research from training‑time scaling to test‑time computation, detailing the underlying principles, new scaling laws, verification techniques, reinforcement‑learning pipelines, and practical methods for distilling reasoning capabilities into smaller models.

DeepSeek-R1Large Language Modelsinference
0 likes · 18 min read
Understanding Inference Large Language Models: DeepSeek‑R1 and the Rise of Test‑Time Computation
ShiZhen AI
ShiZhen AI
Feb 27, 2025 · Artificial Intelligence

Step‑by‑Step Guide: Build Your Own Lerobot SO‑ARM100 Robotic Arm from Scratch

This article walks you through the entire process of assembling a low‑cost Lerobot SO‑ARM100 6‑DOF robotic arm, configuring its Feetech servos, calibrating motion, adding dual cameras for teleoperation, collecting a dataset, and training a reinforcement‑learning policy locally or on cloud GPUs, with detailed troubleshooting tips and code examples.

LerobotPythonSO-ARM100
0 likes · 16 min read
Step‑by‑Step Guide: Build Your Own Lerobot SO‑ARM100 Robotic Arm from Scratch
Tencent Technical Engineering
Tencent Technical Engineering
Feb 26, 2025 · Artificial Intelligence

Engineers' Perspectives on DeepSeek: Technical Innovations and Implications

Thirteen engineers praise DeepSeek’s open‑source, reinforcement‑learning‑driven architecture—using FP8 storage and SFT‑free training—to deliver GPT‑4‑level reasoning at one‑twentieth the cost, enabling single‑GPU deployment, lowering barriers for academia and startups, and prompting notable market reactions that could democratize advanced AI.

AI cost reductionDeepSeekFP8
0 likes · 9 min read
Engineers' Perspectives on DeepSeek: Technical Innovations and Implications
DevOps
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
Architect
Architect
Feb 22, 2025 · Artificial Intelligence

How Open‑Source Projects Reproduced DeepSeek‑R1 and Pushed LLM Limits

This article reviews the most notable open‑source reproductions of DeepSeek‑R1—including Open R1, OpenThoughts, LIMO and DeepScaleR—detailing their data pipelines, training steps, reinforcement‑learning strategies, dataset constructions, and benchmark results that demonstrate how small, high‑quality data can rival massive‑scale models.

AI researchDataset ConstructionDeepSeek-R1
0 likes · 26 min read
How Open‑Source Projects Reproduced DeepSeek‑R1 and Pushed LLM Limits
Python Programming Learning Circle
Python Programming Learning Circle
Feb 20, 2025 · Artificial Intelligence

Building a StarCraft II AI Bot with DeepMind's pysc2 in Python

This article provides a step‑by‑step guide, complete with Python code examples, for creating a Protoss AI bot using DeepMind's pysc2 library to mine resources, construct buildings, train units, implement scouting, and execute attack strategies against increasingly difficult computer opponents in StarCraft II.

AI botDeepMindStarCraft II
0 likes · 28 min read
Building a StarCraft II AI Bot with DeepMind's pysc2 in Python
Architect's Alchemy Furnace
Architect's Alchemy Furnace
Feb 19, 2025 · Artificial Intelligence

DeepSeek’s Self‑Correction: Transforming AI Reliability and Safety

The article explores DeepSeek’s innovative self‑correction system—combining a Mixture‑of‑Experts architecture with reinforcement‑learning feedback—to achieve real‑time error detection, dynamic knowledge‑graph updates, and enhanced safety in high‑risk fields like autonomous driving and medical diagnostics.

AI safetyDeepSeekMixture of Experts
0 likes · 9 min read
DeepSeek’s Self‑Correction: Transforming AI Reliability and Safety
Tencent Technical Engineering
Tencent Technical Engineering
Feb 19, 2025 · Artificial Intelligence

Reproduction and Analysis of DeepSeek R1/R1‑zero Reinforcement Learning Experiments

This note surveys four open‑source reproductions of DeepSeek R1/R1‑zero reinforcement‑learning pipelines, re‑implements their training on math and logic datasets using Qwen‑based models, shows that format‑plus‑accuracy rewards improve long‑chain reasoning though stability and scaling remain challenges, and outlines future directions for large‑scale RL and business deployment.

DeepSeek-R1large language modellong chain of thought
0 likes · 39 min read
Reproduction and Analysis of DeepSeek R1/R1‑zero Reinforcement Learning Experiments
AI Algorithm Path
AI Algorithm Path
Feb 18, 2025 · Artificial Intelligence

Build DeepSeek‑R1 from Scratch: Complete Training Process with Code Walkthrough

This article provides a step‑by‑step, code‑first guide to reproducing DeepSeek‑R1 from the ground up, covering model selection, dataset preparation, custom reward functions, GRPO reinforcement‑learning training, supervised fine‑tuning, reasoning‑oriented RL, rejection sampling, and model distillation.

DeepSeek-R1LLM trainingPython
0 likes · 48 min read
Build DeepSeek‑R1 from Scratch: Complete Training Process with Code Walkthrough
DataFunTalk
DataFunTalk
Feb 16, 2025 · Artificial Intelligence

Understanding Reasoning LLMs: DeepSeek R1 Variants, Inference‑Time Scaling, and Training Strategies

This article explains what reasoning language models are, outlines their strengths and weaknesses, details DeepSeek R1's three variants and their training pipelines—including pure reinforcement learning, SFT + RL, and distillation—while also discussing inference‑time scaling techniques and related research such as Sky‑T1 and TinyZero.

DeepSeekInference Scalingmodel distillation
0 likes · 16 min read
Understanding Reasoning LLMs: DeepSeek R1 Variants, Inference‑Time Scaling, and Training Strategies
JD Cloud Developers
JD Cloud Developers
Feb 13, 2025 · Artificial Intelligence

Unlocking DeepSeek R1: Concepts, Training Secrets, and Real-World Experiments

This article demystifies DeepSeek R1 by explaining key concepts such as online search integration and the R1 model, detailing its two‑phase training pipeline, core techniques like iterative data enhancement, and showcases practical reproductions, benchmark tests, and deployment examples for AI developers.

DeepSeekknowledge distillationlarge language model
0 likes · 12 min read
Unlocking DeepSeek R1: Concepts, Training Secrets, and Real-World Experiments
AI Algorithm Path
AI Algorithm Path
Feb 12, 2025 · Artificial Intelligence

Essential DeepSeek‑R1 Reading List: Papers Behind the 2025 Hottest LLM

This article compiles a curated reading list of foundational and recent research papers—from the original Transformer to chain‑of‑thought, mixture‑of‑experts, and reinforcement‑learning studies—that together explain the breakthroughs behind DeepSeek‑R1 and guide readers through the technical evolution of modern large language models.

DeepSeekMixture of ExpertsTransformer
0 likes · 15 min read
Essential DeepSeek‑R1 Reading List: Papers Behind the 2025 Hottest LLM
Architects' Tech Alliance
Architects' Tech Alliance
Feb 10, 2025 · Industry Insights

What Makes DeepSeek’s New V3 Model Rival GPT‑4o? A Deep Dive into Large‑Scale AI

This article explains what defines a large AI model, compares parameter scales of GPT‑3, GPT‑4 and M6, and analyzes DeepSeek’s recent releases—V3, R1, and Janus‑Pro—highlighting their benchmark performance, reinforcement‑learning techniques, and cost efficiency versus leading proprietary models.

AI BenchmarkDeepSeeklarge models
0 likes · 5 min read
What Makes DeepSeek’s New V3 Model Rival GPT‑4o? A Deep Dive into Large‑Scale AI
Top Architect
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
Architects' Tech Alliance
Architects' Tech Alliance
Feb 9, 2025 · Artificial Intelligence

How DeepSeek R1 Replicates OpenAI o1 Using Large‑Scale Reinforcement Learning

The article provides an in‑depth technical analysis of DeepSeek R1, explaining how it reproduces OpenAI o1's reasoning abilities through rule‑based large‑scale reinforcement learning, mixed SFT data, and efficient scaling, while discussing its broader impact on AI model development and capability density trends.

AI industryCapability DensityDeepSeek
0 likes · 19 min read
How DeepSeek R1 Replicates OpenAI o1 Using Large‑Scale Reinforcement Learning
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Feb 8, 2025 · Artificial Intelligence

Analyzing DeepSeek R1 Inference Projects: Source Code, Cold‑Start, and Scaling Techniques

This article examines DeepSeek R1’s three breakthroughs, its low‑cost optimizations that bypass CUDA, and the resulting impact on the AI ecosystem, then provides a detailed technical review of seven open‑source reproductions—Open‑R1, Tiny‑Zero, SimpleScaling‑S1, and simpleRL‑reason—covering their architectures, reinforcement‑learning pipelines, and code implementations.

DeepSeekInference ScalingLarge Language Models
0 likes · 10 min read
Analyzing DeepSeek R1 Inference Projects: Source Code, Cold‑Start, and Scaling Techniques
JavaEdge
JavaEdge
Feb 8, 2025 · Artificial Intelligence

Why DeepSeek R1 Rivals ChatGPT o1: Architecture, Training, and Cost Insights

This article provides a detailed technical analysis of DeepSeek's R1 large language model, covering its background, architecture, training methods, hardware optimizations, performance claims, user impressions, deployment options, and the challenges of reproducing its results.

AI trainingDeepSeekGPU Cost
0 likes · 16 min read
Why DeepSeek R1 Rivals ChatGPT o1: Architecture, Training, and Cost Insights
Architect
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
Architect
Architect
Feb 3, 2025 · Artificial Intelligence

How DeepSeek‑R1 Uses Pure Reinforcement Learning to Match OpenAI’s o1

This article presents DeepSeek‑R1 and DeepSeek‑R1‑Zero, two next‑generation LLMs trained with pure reinforcement learning and multi‑stage fine‑tuning, details their GRPO training framework, model‑distillation pipeline, open‑source release, and evaluation results that rival OpenAI’s o1‑1217 across reasoning, knowledge, and coding benchmarks.

DeepSeekLLM evaluationLarge Language Models
0 likes · 10 min read
How DeepSeek‑R1 Uses Pure Reinforcement Learning to Match OpenAI’s o1
Cognitive Technology Team
Cognitive Technology Team
Feb 3, 2025 · Artificial Intelligence

DeepSeek R1 Introduces Group‑Related Policy Optimization for Advanced Reasoning in Large Language Models

DeepSeek AI’s new open‑source model DeepSeek‑R1 leverages a novel Group‑Related Policy Optimization (GRPO) reinforcement‑learning framework and multi‑stage training to dramatically boost complex reasoning performance, achieving AIME 2024 Pass@1 scores comparable to OpenAI’s o1 model.

AIDeepSeekGRPO
0 likes · 4 min read
DeepSeek R1 Introduces Group‑Related Policy Optimization for Advanced Reasoning in Large Language Models
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Jan 28, 2025 · Artificial Intelligence

How DeepSeek’s $5.5 M Training Cost Triggered a $1 T Market Collapse and Redefined AI Innovation

DeepSeek’s low‑cost, open‑source AI model, trained for $5.5 million, caused Nvidia’s market value to plunge by nearly $6 trillion, outperformed proprietary rivals on benchmarks, slashed token costs to $0.14, and sparked a global debate on AI democratization and the end of compute‑centric dominance.

AI democratizationDeepSeekNvidia market impact
0 likes · 8 min read
How DeepSeek’s $5.5 M Training Cost Triggered a $1 T Market Collapse and Redefined AI Innovation
Baobao Algorithm Notes
Baobao Algorithm Notes
Jan 22, 2025 · Artificial Intelligence

Can RL‑Only Training Make LLMs Beat OpenAI‑o1? Inside DeepSeek‑R1’s Architecture and Results

DeepSeek‑R1’s open‑source series demonstrates that reinforcement‑learning‑only training can match top‑tier models like OpenAI‑o1, while a small amount of SFT further improves readability; the article dissects its technical report, training pipeline, reward design, distillation strategy, benchmark outcomes, and remaining challenges.

DeepSeekSupervised Fine‑Tuninglarge language model
0 likes · 11 min read
Can RL‑Only Training Make LLMs Beat OpenAI‑o1? Inside DeepSeek‑R1’s Architecture and Results
php Courses
php Courses
Jan 21, 2025 · Artificial Intelligence

Building Reinforcement Learning Algorithms with PHP

This article introduces reinforcement learning, explains its core concepts, and demonstrates how to implement a simple reinforcement learning algorithm in PHP using libraries such as Keras or TensorFlow, providing complete example code for environment and agent classes and outlining training and testing steps.

Artificial IntelligenceCode ExamplePHP
0 likes · 5 min read
Building Reinforcement Learning Algorithms with PHP
Alimama Tech
Alimama Tech
Jan 8, 2025 · Artificial Intelligence

Model-Based Reinforcement Learning Auto‑Bidding Algorithms for Online Advertising

The paper introduces a model‑based reinforcement‑learning auto‑bidding framework that learns a neural‑network environment model from real logs, generates confidence‑aware virtual data fused with real data, and employs the COMBO+MICRO stabilizer and a Lagrange‑dual method for ROI‑constrained bidding, delivering up to 6.8 % higher consumption, 5 % GMV growth and 3.7 % ROI improvement on Alibaba’s platform.

Online Advertisingauto-biddingbudget constrained bidding
0 likes · 22 min read
Model-Based Reinforcement Learning Auto‑Bidding Algorithms for Online Advertising
DataFunSummit
DataFunSummit
Jan 5, 2025 · Artificial Intelligence

Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)

The article presents a CIKM‑2024 paper that introduces MODRL‑TA, a multi‑objective deep reinforcement learning system combining multi‑objective Q‑learning, a cross‑entropy‑based decision‑fusion algorithm, and a progressive data‑augmentation pipeline to dynamically allocate search traffic on JD.com, with both offline and online experiments showing substantial gains in CTR, CVR, and overall platform performance.

cross-entropy methoddeep learninge-commerce
0 likes · 14 min read
Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)
JD Retail Technology
JD Retail Technology
Dec 26, 2024 · Artificial Intelligence

Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)

MODRL‑TA is a multi‑objective deep reinforcement learning framework that unites independent Q‑learning agents, a cross‑entropy‑based decision‑fusion module, and progressive data‑augmentation to overcome cold‑start and multi‑objective trade‑offs in e‑commerce traffic allocation, delivering up to 18% more impressions, 4% higher CTR and 5% higher CVR in live tests.

deep learninge-commercemulti-objective
0 likes · 14 min read
Multi‑Objective Deep Reinforcement Learning Framework for E‑commerce Traffic Allocation (MODRL‑TA)
Alimama Tech
Alimama Tech
Dec 17, 2024 · Artificial Intelligence

AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games

AuctionNet is a newly introduced benchmark that recreates a massive, realistic online advertising auction environment using latent diffusion‑generated traffic data, provides an 80 GB dataset of 5 × 10⁸ logs from 48 bidding agents, and offers baseline evaluations—including an Online LP that outperforms others—supporting thousands of fair NeurIPS 2024 competition submissions and open‑source tools for large‑scale game decision‑making research.

Generative ModelsOnline Advertisingauto-bidding
0 likes · 15 min read
AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games
Kuaishou Tech
Kuaishou Tech
Dec 17, 2024 · Artificial Intelligence

NeurIPS 2024 Auto‑Bidding in Large‑Scale Auctions: Kuaishou Team Wins Both General and AIGB Tracks

The NeurIPS 2024 Auto‑Bidding competition attracted over 15,000 submissions and 1,500 teams, featuring two tracks—General and AI‑Generated Bidding—where Kuaishou’s commercial algorithm team secured first place in both by leveraging reinforcement‑learning‑based online exploration and a decision‑transformer‑driven generative approach, achieving more than a 5% lift in ad revenue.

AdvertisingGenerative ModelsKuaishou
0 likes · 13 min read
NeurIPS 2024 Auto‑Bidding in Large‑Scale Auctions: Kuaishou Team Wins Both General and AIGB Tracks
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 7, 2024 · Artificial Intelligence

What Is Reinforcement Fine-Tuning (RFT) and How Does It Supercharge LLMs?

Reinforcement Fine-Tuning (RFT) combines supervised fine‑tuning with reinforcement learning to teach large language models to reason more effectively, using separate training and validation datasets, graders, and PPO optimization, and has shown superior performance on tasks like gene prediction and math reasoning compared to standard SFT.

AILarge Language Modelsmachine learning
0 likes · 8 min read
What Is Reinforcement Fine-Tuning (RFT) and How Does It Supercharge LLMs?
AI Product Manager Community
AI Product Manager Community
Dec 7, 2024 · Artificial Intelligence

How Reinforcement Fine-Tuning (RFT) Is Redefining AI Customization

Reinforcement Fine-Tuning (RFT), unveiled at OpenAI’s 12‑day launch, introduces a feedback‑loop approach that transforms generic models into specialized experts using reinforcement learning, small data, and domain‑specific scorers, offering product managers a powerful tool for rapid, cost‑effective AI customization across industries.

AI CustomizationFine-tuningmachine learning
0 likes · 7 min read
How Reinforcement Fine-Tuning (RFT) Is Redefining AI Customization
Bilibili Tech
Bilibili Tech
Dec 6, 2024 · Artificial Intelligence

Ensemble-based Offline-to-Online Reinforcement Learning (ENOTO): Methodology, Experiments, and Analysis

ENOTO introduces ensemble Q‑networks into the offline‑to‑online reinforcement‑learning pipeline, using minimum‑Q and uncertainty‑driven exploration to stabilize fine‑tuning, boost learning efficiency, and achieve 10‑25 % higher cumulative returns with minimal online interaction across MuJoCo and AntMaze benchmarks.

AntMazeENOTOEnsemble Q-Networks
0 likes · 16 min read
Ensemble-based Offline-to-Online Reinforcement Learning (ENOTO): Methodology, Experiments, and Analysis
Alimama Tech
Alimama Tech
Dec 4, 2024 · Artificial Intelligence

AIGB: Generative Auto‑Bidding via Diffusion Modeling

AIGB, introduced by Alibaba Mama in 2023, reframes large‑scale ad‑auction auto‑bidding as a generative sequence task using diffusion models, achieving up to 5 % GMV gains, improved stability and interpretability, and is now commercialized, open‑sourced, and featured in a NeurIPS‑endorsed competition.

AIGenerative Modelsauto-bidding
0 likes · 12 min read
AIGB: Generative Auto‑Bidding via Diffusion Modeling
Model Perspective
Model Perspective
Dec 3, 2024 · Artificial Intelligence

How Recommendation Algorithms Shape Our Habits—and What You Can Do About It

The article examines how recommendation algorithms reinforce user preferences, turning habits into stable feedback loops, and proposes mathematical models and practical strategies to introduce diversity and break behavioral fixation in the age of algorithmic personalization.

Diversitybehavioral modelinghabit formation
0 likes · 7 min read
How Recommendation Algorithms Shape Our Habits—and What You Can Do About It
DaTaobao Tech
DaTaobao Tech
Oct 30, 2024 · Artificial Intelligence

Understanding OpenAI o1: Chain‑of‑Thought, Scaling Laws, and Training Strategies

The article explains how OpenAI’s o1 model leverages chain‑of‑thought prompting, dual‑system cognitive theory, and new scaling laws—pre‑training on code/math and post‑training reinforcement with step‑wise reward models—to achieve superior reasoning, safety, and performance over GPT‑4, heralding a shift toward models that learn to think.

LLMSafetychain-of-thought
0 likes · 42 min read
Understanding OpenAI o1: Chain‑of‑Thought, Scaling Laws, and Training Strategies
Baobao Algorithm Notes
Baobao Algorithm Notes
Oct 21, 2024 · Artificial Intelligence

Unraveling RLHF: From PPO to DPO and Beyond – A Comprehensive Guide

This article provides a thorough, four‑part overview of RLHF for large language models, covering preference‑optimization algorithms (PPO‑based and offline RL approaches), reward‑model training techniques, inference‑time exploration strategies, and practical implementation details including the OpenRLHF framework and resource‑allocation tricks.

DPOLLM OptimizationOpenRLHF
0 likes · 27 min read
Unraveling RLHF: From PPO to DPO and Beyond – A Comprehensive Guide
Baobao Algorithm Notes
Baobao Algorithm Notes
Oct 15, 2024 · Artificial Intelligence

How DPO Simplifies RLHF: A Deep Dive into Direct Preference Optimization

This article breaks down how Direct Preference Optimization (DPO) mathematically reduces the two‑stage RLHF pipeline into a single‑stage SFT process, explains the underlying loss transformations, and discusses DPO's practical limitations and trade‑offs for large language model alignment.

DPODirect Preference OptimizationRLHF
0 likes · 9 min read
How DPO Simplifies RLHF: A Deep Dive into Direct Preference Optimization
DataFunSummit
DataFunSummit
Sep 27, 2024 · Artificial Intelligence

Advances in Educational Large Language Models for Youth Programming and Personalized Learning

The presentation by Dr. Su Yu outlines challenges such as data sparsity and delayed learning effects in AI‑driven education, introduces three technical breakthroughs—domain‑specific LLM training, small‑knowledge learning via hierarchical knowledge graphs, and reinforcement‑based cognitive recommendation—and showcases product applications like the Frog Programming Platform, AI Programming Learning Machine, and digital‑human AI recorded courses.

AI educationKnowledge GraphPersonalized Learning
0 likes · 18 min read
Advances in Educational Large Language Models for Youth Programming and Personalized Learning
Model Perspective
Model Perspective
Sep 27, 2024 · Artificial Intelligence

Modeling Everyday Learning: From Reinforcement to Social Learning

The article explores how everyday decision‑making can be modeled using reinforcement learning and social learning frameworks, illustrating their strengths, limitations, and combined insights for understanding individual and collective behavior.

AIbehavioral modelingdecision making
0 likes · 8 min read
Modeling Everyday Learning: From Reinforcement to Social Learning
Architect
Architect
Sep 26, 2024 · Artificial Intelligence

Decoding OpenAI o1: How RL‑LLM Fusion Powers Next‑Gen Reasoning

This article provides a detailed technical analysis of OpenAI’s o1 model, exploring its enhanced logical reasoning, the likely use of reinforcement learning with hidden chain‑of‑thought generation, multi‑model architecture, training data pipelines, reward modeling, and how these innovations could reshape AI safety and scaling strategies.

AI safetyLLMOpenAI o1
0 likes · 43 min read
Decoding OpenAI o1: How RL‑LLM Fusion Powers Next‑Gen Reasoning
JD Tech Talk
JD Tech Talk
Sep 23, 2024 · Artificial Intelligence

JD Advertising R&D: AI‑Driven Solutions for Traffic Valuation, Multimodal Understanding, Auction Mechanisms, Generative Recommendation, and Large‑Model Engineering

The JD Advertising R&D team applies cutting‑edge AI techniques—including query intent models, multimodal representation pipelines, reinforcement‑learning‑based auction mechanisms, generative recommendation with quantized product tokens, and large‑model infrastructure—to boost traffic valuation, ad relevance, revenue, and creative generation across the platform.

AIAdvertisingMultimodal
0 likes · 19 min read
JD Advertising R&D: AI‑Driven Solutions for Traffic Valuation, Multimodal Understanding, Auction Mechanisms, Generative Recommendation, and Large‑Model Engineering
Data Thinking Notes
Data Thinking Notes
Sep 13, 2024 · Artificial Intelligence

How OpenAI’s o1 Series Redefines Complex Reasoning and AI Safety

OpenAI’s new o1 series, including o1‑preview and o1‑mini, leverages reinforcement‑learning‑based chain‑of‑thought reasoning to achieve superior performance on academic exams, coding contests, and safety benchmarks, offering faster, cost‑effective options while advancing AI alignment and human‑preference evaluation.

AI safetyCodingOpenAI
0 likes · 15 min read
How OpenAI’s o1 Series Redefines Complex Reasoning and AI Safety
Tencent Advertising Technology
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.

AdvertisingNatural Language UnderstandingRLHF
0 likes · 14 min read
Enhancing Reinforcement Learning with Label-Sensitive Reward for Natural Language Understanding
Tencent Advertising Technology
Tencent Advertising Technology
Aug 13, 2024 · Artificial Intelligence

Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors

This paper investigates selection bias in large language models for multiple‑choice tasks, proposes metrics to quantify symbol‑content binding, introduces Reweighting Symbol‑Content Binding (RSCB) and Point‑wise Intelligent Feedback (PIF) methods, and demonstrates their effectiveness in reducing bias and improving accuracy, including a real‑world Tencent advertising feature‑evaluation deployment.

Symbol Bindingmultiple choicepointwise feedback
0 likes · 16 min read
Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors
Model Perspective
Model Perspective
Jul 31, 2024 · Artificial Intelligence

How Monte Carlo Tree Search Powers AlphaGo and Beyond: A Deep Dive

Monte Carlo Tree Search (MCTS) is a statistical heuristic algorithm that builds decision trees through selection, expansion, simulation, and backpropagation, enabling breakthroughs like AlphaGo’s victory and finding applications in robotics, autonomous driving, finance, and bioinformatics.

AI ApplicationsAlphaGoMCTS
0 likes · 7 min read
How Monte Carlo Tree Search Powers AlphaGo and Beyond: A Deep Dive
Model Perspective
Model Perspective
Jul 30, 2024 · Artificial Intelligence

Your Complete AI Learning Roadmap: From Basics to Large Model Mastery

This guide presents a comprehensive AI learning roadmap, dividing study into five progressive stages—from foundational math and programming to core deep‑learning and reinforcement‑learning techniques, large‑model training, industry applications, and future trends—plus curated book lists, tool recommendations, and practical RAG tutorials.

AI learning roadmapAI resourcesRAG
0 likes · 9 min read
Your Complete AI Learning Roadmap: From Basics to Large Model Mastery
Alimama Tech
Alimama Tech
Jul 29, 2024 · Artificial Intelligence

Generative Auto-bidding via Diffusion Modeling (AIGB)

The paper presents AIGB, a generative auto‑bidding framework that replaces reinforcement‑learning with a conditional diffusion model to generate optimal bidding trajectories, and demonstrates through offline benchmarks and Alibaba’s online A/B tests that it consistently outperforms RL baselines, boosting buy count, GMV, and ROI while maintaining low latency.

Generative ModelsMarketing AIOnline Advertising
0 likes · 18 min read
Generative Auto-bidding via Diffusion Modeling (AIGB)
php Courses
php Courses
Jul 29, 2024 · Artificial Intelligence

Building Reinforcement Learning Algorithms with PHP

This article explains the fundamentals of reinforcement learning, demonstrates how PHP can be used with neural‑network libraries such as Keras or TensorFlow to implement a simple reinforcement‑learning agent, provides a complete PHP code example, and discusses its potential applications.

AICode Examplereinforcement learning
0 likes · 5 min read
Building Reinforcement Learning Algorithms with PHP
Kuaishou Tech
Kuaishou Tech
Jul 17, 2024 · Artificial Intelligence

Key Technical Innovations in Kuaishou’s “Kuaiyi” Large Model and Its Real-World Applications

The article details Kuaishou’s development of the 175B “Kuaiyi” multimodal large model, presenting eight novel technical innovations—from Temporal Scaling Law and MiLe Loss to MoE‑enhanced reward modeling—and describes how these advances enable high‑performance AI services such as the AI Xiao Kuai chatbot across diverse real‑world scenarios.

AI ApplicationsModel Optimizationlarge language model
0 likes · 12 min read
Key Technical Innovations in Kuaishou’s “Kuaiyi” Large Model and Its Real-World Applications
Alimama Tech
Alimama Tech
Jul 15, 2024 · Artificial Intelligence

Why Auto‑Bidding in Large‑Scale Auctions Is the Hottest NeurIPS Challenge

The article explains how NeurIPS ranks among top AI conferences, introduces the newly selected “Auto‑Bidding in Large‑Scale Auctions” competition, outlines its technical background, four generations of bidding strategies—from classic control to generative models—and details the competition’s tracks, rewards, and how researchers can participate.

AdvertisingCompetitionNeurIPS
0 likes · 12 min read
Why Auto‑Bidding in Large‑Scale Auctions Is the Hottest NeurIPS Challenge