Reinforcement Learning

Qwen2.5 Technical Report Analysis: 18 Trillion Token Training

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Qwen2.5 Technical Report Analysis: 18 Trillion Token Training

Introduction The development of large language models (LLMs) is advancing rapidly, with each significant update potentially bringing substantial performance improvements and expanding application scenarios. Against this backdrop, Alibaba’s latest release of the Qwen2.5 series models has garnered widespread attention. This technical report provides a detailed overview of the development process, innovations, and performance of Qwen2.5, … Read more

Qwen2.5 Technical Report

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Qwen2.5 Technical Report

In December 2024, the paper “Qwen2.5 Technical Report” from Tongyi Qianwen was released. This report introduces Qwen2.5, a series of comprehensive large language models (LLMs) designed to meet diverse needs. Compared to previous iterations, Qwen 2.5 has made significant improvements in both pre-training and post-training phases. In terms of pre-training, the high-quality pre-training dataset has … Read more

Differences and Connections Between AI Agents and Agentic AI

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Differences and Connections Between AI Agents and Agentic AI

The differences and connections between AI Agents and Agentic AI AI Agents and Agentic AI are two important yet distinct concepts in the field of artificial intelligence, with both connections and significant differences between them. Connections 1.Technical Foundation: Both are built on artificial intelligence and machine learning technologies, relying on capabilities such as perception, reasoning, … Read more

TensorFlow 2 Models: Deep Reinforcement Learning

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TensorFlow 2 Models: Deep Reinforcement Learning

By / Li Xihan, Google Developers Expert This article is excerpted from “Simple and Brutal TensorFlow 2”, reply “Manual” to get the collection. It should have been introduced long ago, the deep reinforcement learning in TensorFlow, yes, it is finally done! This article will introduce the process of implementing the Q-learning algorithm using TensorFlow in … Read more

Applications of Generative Adversarial Networks (GANs) in NLP

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Applications of Generative Adversarial Networks (GANs) in NLP

This article is reproduced with permission from the WeChat public account Paper Weekly (ID: paperweekly). Paper Weekly shares interesting papers in the field of natural language processing every week. “In-depth Analysis: GAN Models and Their Progress in 2016” [1] provides a detailed introduction to the progress of GANs over the past year, which is highly … Read more

Introduction and Practice of LangGraph Based on Large Model Agent

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Introduction and Practice of LangGraph Based on Large Model Agent

How to Obtain Resources 1. Follow the public account below, and click【Like】 and 【View】 2. Click 【Get Course】 to obtain this material. Resources are from Baidu Cloud Disk:《Introduction and Practice of LangGraph Based on Large Model Agent》 Introduction and Practice of LangGraph Based on Large Model Agent In the field of artificial intelligence, with the … Read more

The Integration of Gaming, AI Agents, and Cryptocurrency

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The Integration of Gaming, AI Agents, and Cryptocurrency

We are transitioning from a “play-to-earn” model to a more exciting era: games that are rich in genuine fun and infinitely scalable. Author: Sid, IOSG Ventures Original Title: IOSG Weekly Brief | The Integration of Gaming, AI Agents, and Cryptocurrency #260 Cover: Photo by Lorenzo Herrera on Unsplash This article is for educational exchange only … Read more

When RNN Meets Reinforcement Learning: Building General Models for Space

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When RNN Meets Reinforcement Learning: Building General Models for Space

You may be familiar with reinforcement learning, and you may also know about RNNs. What sparks can these two relatively complex concepts in the world of machine learning create together? Let me share a few thoughts. Before discussing RNNs, let’s first talk about reinforcement learning. Reinforcement learning is gaining increasing attention; its importance can be … Read more

Kimi K1.5: Multimodal Reinforcement Learning Achieves Performance and Efficiency

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Kimi K1.5: Multimodal Reinforcement Learning Achieves Performance and Efficiency

Finally, Kimi has been updated! I’ve been looking forward to this. It is said to be in grayscale: but my interface still looks like this. Let’s wait a bit and try later~ Let’s read the paper together and see what technical details have changed. Address: https://github.com/MoonshotAI/Kimi-k1.5/blob/main/Kimi_k1.5.pdf The pre-training methods of large language models (LLMs) have … Read more

Kimi K1.5: Scaling Reinforcement Learning with LLMs

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Kimi K1.5: Scaling Reinforcement Learning with LLMs

1. Title: KIMI K1.5: SCALING REINFORCEMENT LEARNING WITH LLMS Link: https://github.com/MoonshotAI/kimi-k1.5 2. Authors and Key Points: 1- Authors The paper was published by: Kimi Team of the Dark Side of the Moon 2- Key Points 1. Core Content • Background and Motivation: • Traditional language model pre-training methods (based on next-word prediction) perform well in … Read more