fatima gasmi

Hi everyone, I wanted to share with my brief write up about basic optimization algorithms used in deep learning. I start by giving the convex optimization backgrounds where these algorithms were conceived. Then following the amazing ML book by Shai Shalev-Shwartz and Shai Ben-David, I discuss the intuitive interpretation of Gradient Descent and SGD. Lastly, I do a deep dive of the absolutely amazing ICML 2013 paper by Ilya Sutskever & Hinton  ( “ On the importance of initialization and momentum in deep learning.”) which gives wonderful re interpretation of the classical momentum algo ; re -derives the Nesterov Accelerated Gradient (NAG, 1983) as a form of momentum and talks about their different experimentations regarding how these algos vs SGD vs 2nd order approximate Newton methods fare for deep learning, and how they deal with high curvature. Thus overall, this blog is supposed to be a refresher for the basics, before we dive into the newer, faster algorithms like Adam & other Adaptive algorithms in the next article. I would like to thank my Department of CSE, IIT Hyderabad professors, especially Vineeth N Balasubramanian Sir who taught us 2 courses - ML & deep learning and Saketha Nath Jagarlapudi Sir who taught us convex optimization theory and convex optimization algorithms, without whom I wouldn't have the necessary foundations to undertake this exploration. #deeplearning #optimization #gradientdescent #sgd #momentum #nesterov #nag #hinton #paperreview

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Hey, ready to summarize the last AI-week ? 1) DeepSeek-V3: Open-Source Model Rivals Leading AI Systems 2) OpenAI Restructures as For-Profit Public Benefit Corporation 3) Meta Introduces AI-Generated Users to Facebook and Instagram - Update : Meta Destroyed the AI-Generated Users ;) 4) VCs Predict AI Agents Transforming Enterprises by 2025 5) Microsoft and OpenAI Define AGI with $100 Billion Benchmark 6) MIT's SciAgents: AI Framework for Autonomous Hypothesis Generation 7) AI Model Automates Discovery of Novel Artificial Life Forms 8) AI-Driven Jobs Witness a Huge Surge in Demand 9) AI-Generated Phishing Emails Increasingly Target Corporate Executives 10) Science's Security Dilemma Amid China's Rise More on https://lnkd.in/epJkWNPA.

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Hey everyone, in my previous article I had covered the basics of gradient descent, SGD, Momentum & NAG. Here we would cover a new set of optimization, called Adaptive learning algorithms. For the first algo ADAGRAD (2011) , we first discuss the more mathematical presentation from the original paper and then give a more intuitive and simpler explanation from the Adadelta(2012) paper which tries to fix its issue of prematurely slowing down. Next, We will first do an in-depth coverage of RProp(1993), and how RMSProp(2012) was created to adapt RProp to stochastic settings. Next we will discuss Adam (2015) in detail. Adam has been the one of the most popular optimization algorithms, but what we tend to overlook that while it can accelerate the initial phases of training ( in terms of lowering the loss), its generalization capabilities are far worse than SGD+Momentum. There has been multiple attempts to improve its shortcomings -- like Nadam(2016), AMSGRAD(2018) , ADABOUND & AMSBOUND(2019), RAdam( 2019/2020) and Adan(2023/2024). We go through them very briefly Last but not least, we will do a deep dive of the highly successful AdamW (2017/19) algorithm which is highly popular in LLM training these days. #deeplearning #optimization #rmsprop #Adam #AdamW

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New blog post! One of the most popular implementations of Generative AI is this mechanism to incorporate your own data called retrieval augmented generation (RAG). Given that we want to ensure that our RAG pipelines remain performant and effective over time, this post takes a deep dive into a framework to perform this evaluation called Ragas. In this post, we cover how to synthesize a dataset to play with Ragas, how to calculate the Ragas metrics in Python code, and how each of the individual metrics is calculated “under the hood.” #RAG #GenAI #AI #Ragas #DataScience https://lnkd.in/gsvJrCj9

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Meta has just released their long-awaited #Llama #3 models. Everyone's talking about it, let me give it a try 😅! Some observations: ☑ #Llama #8B and #70B are available, and the #400B is currently in training ⁉ Yes... well, who cares, right? We see new models every morning? Well, not really, these models are a bit different. Not in terms of innovation – practically the same as #Llama #2 – but rather in the meticulous work done on the quality and quantity of data. ✅ For the first time, we have a model of 8B (and 70B as well) trained with more than 15 trillion tokens! For comparison, #Llama #1 used 1T, and #Llama #2 used 2T. That's 8 times more data. To give you an idea, 15T tokens =~ 69 terabytes. Training an #8B model with 15T far exceeds the norms; Chinchilla Scaling suggests 20 tokens per parameter, which would require 160B tokens for optimal training of a model this size. Here we are talking about 15T, which is 95 larger, that's just astronomical. Is it beneficial? it seems to be the case, at most, it won't hurt. ☑ 5% of the texts are not in English, which is not ideal, but still acceptable because LLMs generalize beyond the training language. ☑ More code in pretraining, as it is proven to improve the reasoning capabilities of LLMs. ☑ What about compute? Training the 8B model required 1.3 million GPU hours (I'm not sure if this includes failures and restarts, or if it's purely 1.3M of training), and the 70B was trained on 6.4 million hours. In total, 7.7 million hours, so at a rate of $2 per hour in a H100 GPU, that amounts to $15 million, not counting the additional costs (engineers, infrastructure, etc.). ☑ Why are these models cool? Because #Llama 1 was the main driver of the proliferation of open-source LLMs (open-weight to be precise). After #Llama 1 (7-65B) and 2 (7-70B), we've seen an explosion of open-source models and research that wouldn't have been possible without these models. ☑ What's next? The next step is #Llama 400B, which is already closing the gap with #GPT4 😲. We will then have all the quantizations, fine-tunings, etc., and we will achieve performances almost close to #GPT4 with smaller models. I thought we'd see this in a year! This will surely motivate OpenAI to release #GPT5 :D. ☑ We will definitely see #Llama with larger context windows than 8k tokens, because 8k is not satisfying. And you? Have you tested these models? Eager to hear your comments on their performance. #LLMs #Llama3 #Llama #Meta

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These days I’ve been in Montpellier, France, to present our last research results on combinatorial optimization problems over temporal graphs to the 31st symposium on temporal representation and reasoning (TIME2024). Check out our paper at https://lnkd.in/da66ref4 Here we discuss a new heuristic-based approach for minimum timeline cover over temporal graphs. The paper provides theoretical results to a problem largely adopted in social network analysis as interpretable network summarization framework to find important events. Particularly, we used it to analyze job postings, to find the most required skills by companies and understand how the market is evolving. We are already working on evolutions of this approach with Deep Learning. Stay tuned for next publications. #knowledgegraph #combinatorialoptimization #networksummarization

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Fine-tuning LLMs: Aligning models with specific customer needs is crucial for real-world success, yet it requires precision to ensure the model adapts effectively without compromising quality. Fine-tuning often introduces risks, such as underfit or overfit layers, which can lead to unpredictable behavior in production. Tools like WeightWatcher are essential for addressing these challenges, offering a data-free diagnostic to evaluate model quality by analyzing the spectral properties of weight matrices. Grounded in the Heavy-Tailed Self-Regularization (HT-SR) theory, WeightWatcher provides insights into layer performance and potential issues, helping developers optimize instruction-tuned models like Llama3.1 and Falcon2 for robust, production-ready use cases. #InstructionFineTuning #LLMEngineering #ModelQuality #CustomerFocusedAI

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Want to test Meta, Google, and Mistral models all on one platform ? **Groq.com** Why Groq? Because speed matters. GroqCloud supports a diverse array of models, enabling you to select the best for your needs: ➡ llama3-8b-8192 by Meta (Context Window: 8,192 tokens) ➡ llama3-70b-8192 by Meta (Context Window: 8,192 tokens) ➡ mixtral-8x7b-32768 by Mistral (Context Window: 32,768 tokens) ➡ gemma-7b-it by Google (Context Window: 8,192 tokens) Groq's commitment to GenAI inference speed is transforming real-time AI applications with its LPU. What is an LPU? ➡ Language Processing Unit, is a revolutionary processing system designed specifically for LANGUAGE applications. Unlike traditional CPUs (Central Processing Units) and GPUs (Graphics Processing Units), which handle a broad range of tasks. LPUs are optimized for the rapid processing demands of LLMs This specialized architecture allows for more efficient handling of sequential data, significantly reducing the time per word processed. ➡ That means you get faster outputs You can run Llama-2 70B at over 300 tokens per second per user!

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𝐁𝐫𝐢𝐥𝐥𝐢𝐚𝐧𝐭 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐚𝐧𝐝 𝐌𝐋 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 The architecture is simply brilliant - e2b runtime - LLM middle layer- - notebook style rendering Massive applications. - fully scalable. - open source - customize - build - integrate Next up - prompt-driven iterative analytics and machine learning. Now exploring ways to incorporate similar solution into REX (rex.tigzig.com / realtime.tigzig.com - my open source analytics toolkit), to provide interactive analysis and machine learning. Check out the video demo on X (I actually saw it on X first): https://lnkd.in/gQXqMRiZ App & Code base : https://lnkd.in/eGmyXh-q

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For those on Twitter/X: Tomorrow (12 noon EST, 6 pm CEST) I will do the third Space dedicated to the third Primer - on AI & ML As a sneak peek to the course, I will speak about the following: - is infinite data infinitely informative? - a statistical equivalent of Gödell undecidability theorem; - how come machine learning seems so powerful, yet it's a massive overfitter? - can we know what a black box is really doing? Come join the Space and do not hesitate to post your questions in the comments below: x.com/i/spaces/1eakb…

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NVIDIA released a powerful AI model with 70 billion parameters that outperforms GPT-4 and Claude 3.5 Sonnet. The best part? It's open-source, meaning anyone can use it. You can try it : https://lnkd.in/dinKV-As

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The AI Revolution in French Public Services. Get ready for this fascinating AI tool called 𝗔𝗹𝗯𝗲𝗿𝘁: Transforming efficiency, accuracy, and responsiveness. Here's how Albert is changing the game: 1. 𝗦𝗼𝘃𝗲𝗿𝗲𝗶𝗴𝗻 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 - Developed internally by the French government. - Controlled entirely by the French administration. 2. 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆𝗶𝗻𝗴 𝗔𝗱𝗺𝗶𝗻𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝘃𝗲 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲𝘀 - Assists public officials in answering citizen queries. - Automates tasks, saving time for both workers and citizens. 3. 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗠𝗮𝘀𝘀𝗶𝘃𝗲 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 - Helps tax agents manage 16 million queries annually. - Pre-processes 4,000 environmental projects each year. 4. 𝗟𝗲𝗴𝗮𝗹 𝗮𝗻𝗱 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲 - Automates transcription of legal hearings. - Creates medical reports and files complaints efficiently. 5. 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗠𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝗘𝗺𝗲𝗿𝗴𝗲𝗻𝗰𝗶𝗲𝘀 - Detects forest fires. - Manages human resources for civil servants. 6. 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗝𝘂𝘀𝘁𝗶𝗰𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 - Assists judges with civil and commercial litigation. - Proposes court rulings better adapted to individual cases. 7. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 - Based on open-source models like Mistral 7B and Llama 33. - Hosted on French government infrastructure for data security. 8. 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗣𝘂𝗯𝗹𝗶𝗰 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲 - Trained to respond to online requests accurately. - Produces verifiable content with the right administrative tone. 9. 𝗚𝗿𝗮𝗱𝘂𝗮𝗹 𝗥𝗼𝗹𝗹𝗼𝘂𝘁 𝗮𝗻𝗱 𝗙𝘂𝘁𝘂𝗿𝗲 𝗣𝗹𝗮𝗻𝘀 - Tested by 60 agents across 30 France Services centers. - Plans to expand usage across more public services. 10. 𝗕𝗲𝘆𝗼𝗻𝗱 𝗔𝗹𝗯𝗲𝗿𝘁: 𝗔𝗿𝗶𝘀𝘁𝗼𝘁𝗹𝗲 𝗳𝗼𝗿 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 - Developing another AI called Aristotle. - Focuses on tutoring and creating quizzes for students. Albert is set to revolutionize public services in France. By automating routine tasks and providing quick access to information, it allows human agents to focus on complex issues and personal interactions with citizens.

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Hi everyone, in this video I wanted to present some basic first and 2nd order methods of optimization algorithm ( SGD, Newton, Momentum, Nesterov) and also the challenges they face in case of deep learning. I talk about the different important academic papers, intuitions from Ian Goodfellow -Bengio’s book & Christopher Bishop - Hugh Bishop ’s book as well Stanford CSC421/2516 class notes that talk about the complex attributes of loss function in case of deep learning like local minima, saddle points and plateau. In contrast to my DL blogs which focus more on the implementation of different algorithms, here I try to *motivate everyone why we need those complex algorithms in the first place*. Here are the list of papers that discussed here in detail  : 1. 2014 NIPS – “Identifying and attacking the saddle point problem in high-dimensional non-convex optimization” co-authored by Yoshua Bengio 2. 2014 JMLR – “The Loss Surfaces of Multilayer Networks” co-authored by Yann LeCun 3. 2017 NIPS – “Visualizing the Loss Landscape of Neural Nets” Feel free to watch at 1.25X or 1.5X SPEED, if required; I speak a bit slow. In stead of giving vague intuitions, I tried to give some *mathematical perspective*. With that goal in mind, I also give a brief refresher on Hessians, eigen values, Taylor Series, spectral decomposition—as and when we require their intuition-- to help us understand clearly the concepts like why Newton’s method does well in plateau and why SGD learning rates need a lot of fine tuning and depends on the maximal eigen value of the Hessian. This way, I try to motivate everyone about the need for Momentum and Adaptive Learning methods (like RMSProp, Adam ). Specifically, I spend the last 15 minutes explaining the mathematical derivations from the Stanford class notes, why the algo will diverge in case the learning rate is too big, and even show some examples in excel with some data points to explain the deck. In retrospect, I could have also done this using diagrams like many other resources do. But did totally love the approach they took in the Stanford classnotes, so that’s fine I guess. Please note, while the above is one motivation behind Adaptive Learning, which , as I cover in the part 2 of my DL blog – helps in making good progress in the initial part of the training ( in terms of lowering training cost fast) – I forgot to mention another which is mentioned in the ADAGRAD paper. Taking inspiration from the concept of TF -IDF, they theorized that sparse features have more predictive power. Hence we need to have more learning rates for features that are sparse. For a more in- depth implementation of these algorithms, please check out my blogs on deep learning. #deeplearning #lossfunctio #optimization #gradientdescent #sgd #momentum #adaptivelearning #learningrate #math #linearalgebra #hessian #eigenvalue #spectraldecomposition #taylorseries

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Everyone is diving into learning about LLMs, and this is great because it's leading to the creation of exceptional resources. . . . #mlops #ai #LanguageModel #MachineLearning #DeepLearning #ArtificialIntelligence #TechInnovation #LLM

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I'm excited to share my latest project: a 𝗥𝗔𝗚 𝗖𝗵𝗮𝘁𝗯𝗼𝘁 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗯𝗼𝘁𝗵 𝗔𝗺𝗮𝘇𝗼𝗻 𝗕𝗲𝗱𝗿𝗼𝗰𝗸 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗯𝗮𝘀𝗲/𝗦𝟯 𝗯𝘂𝗰𝗸𝗲𝘁 and user-uploaded documents to provide accurate, context-aware responses. It also 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝘀 𝘁𝗵𝗲 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲 and 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝘀 𝗰𝗵𝗮𝘁 𝗵𝗶𝘀𝘁𝗼𝗿𝘆. 🔍 Key Features: - 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗨𝗽𝗹𝗼𝗮𝗱 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Support for PDF and TXT files allows users to enrich the chatbot's knowledge base with personal or proprietary documents. - 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺: Utilizes FAISS for vector-based similarity searches. -𝗦𝗼𝘂𝗿𝗰𝗲𝘀 𝗳𝗼𝗿 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀: Ensures transparency and reliability by providing references to the original sources from which information was retrieved. This feature allows users to verify and delve deeper into the provided answers. - 𝗖𝗵𝗮𝘁 𝗛𝗶𝘀𝘁𝗼𝗿𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Maintains a well-organized chat history. - 𝗖𝗹𝗲𝗮𝗿 𝗖𝗵𝗮𝘁 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆: Users can easily reset their conversation history 🛠️ 𝗗𝗶𝗿𝗲𝗰𝘁 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱:  One of the standout features of my RAG Chatbot is it's ability to dynamically retrieve and integrate information directly from Amazon Bedrock knowledge base. Here's how it works: 𝗤𝘂𝗲𝗿𝘆 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴: When a user submits a question, the system first converts the query into a high-dimensional vector using Amazon Bedrock's fully managed bedrock-runtime Embedding API. Leveraging this fully managed API ensures seamless scalability and reliability without the need for manual infrastructure management. 𝗩𝗲𝗰𝘁𝗼𝗿-𝗕𝗮𝘀𝗲𝗱 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: This vector is then used to perform a similarity search against the knowledge base stored in FAISS, identifying the most relevant documents or passages based on cosine similarity. This is achieved through the 𝗯𝗲𝗱𝗿𝗼𝗰𝗸_𝗮𝗴𝗲𝗻𝘁_𝗿𝘂𝗻𝘁𝗶𝗺𝗲.𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲 method, which takes the 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲𝗕𝗮𝘀𝗲𝗜𝗱 and a 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹𝗤𝘂𝗲𝗿𝘆 containing the user's question. By utilizing this method, the chatbot efficiently fetches pertinent information directly from the knowledge base, thereby enhancing performance and reducing latency. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: The retrieved information serves as contextual input for the large language model (LLM), specifically Amazon Bedrock's fully managed Titan-TG1 Large LLM services, which generate a coherent and accurate response tailored to the user's query. If you're interested in seeing this in action or exploring collaboration opportunities, feel free to reach out or try it out yourself! 🔗 Live App: https://lnkd.in/gVEJzM9c 🔗 Github: https://lnkd.in/gKFpjYwB References: https://lnkd.in/gJ88fwwS #GenerativeAI #RAG #AmazonBedrock #FAISS #LangChain #Innovation #NLP #MachineLearning

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📢 Hugging face is directly integrated with GenAI developers favorite tool "langchain"🦜 Get set go!🚀 💡 pip install langchain-huggingface https://lnkd.in/ddHreVrs #huggingface #GenerativeAI #langchain

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Absolutely fascinating! 🚀🔍 The Fudan University and Shanghai AI Laboratory paper on replicating OpenAI's o1 and o3 models offers a unique window into the methodologies behind building state-of-the-art AI reasoning systems. 🧠📊 🔑 Four Key Components of Their Framework: 1️⃣ Policy Initialization 🛠️📚 This step lays the foundation for the AI model’s decision-making capabilities. Proper initialization ensures the model starts with meaningful patterns and heuristics rather than random weights, reducing the effort needed during training. 2️⃣ Reward Design 🎯💡 The success of reinforcement learning hinges on well-defined reward functions. This paper delves into designing incentives that guide the model toward optimal reasoning pathways, balancing exploration and exploitation. 🧠🔄 3️⃣ Search 🔍🌟 Search algorithms are essential for navigating the vast decision space of AI systems. The researchers explore efficient strategies for sampling, evaluating, and selecting reasoning paths, ensuring the system converges on high-quality solutions. ⚙️📈 4️⃣ Reinforcement Learning (RL) 🤖🔗 RL ties everything together, enabling iterative improvement based on feedback signals. The paper examines how reward-driven adjustments fine-tune the policy network to achieve robust reasoning performance. 🔄📊 📝 Why This Paper Stands Out: ✅ Structured Methodology: The clear segmentation into policy, rewards, search, and reinforcement learning simplifies complex ideas into actionable components. ✅ Comprehensive Diagrams: Visual clarity enhances understanding, making the methodologies easier to replicate and adapt. 📊🎓 ✅ Real-World Relevance: The structured approach offers practical guidelines for building scalable AI reasoning systems, bridging academic insight with applied AI development. 🏗️🧠 🌍 Broader Implications: These insights go beyond replicating OpenAI's models—they offer a blueprint for designing AI systems capable of advanced reasoning and decision-making. The methodology is applicable across diverse domains, including autonomous systems, scientific research, and real-time decision engines. 🌟🔑 💬 Final Thoughts: The paper from Fudan University and Shanghai AI Laboratory is a must-read for AI researchers, developers, and enthusiasts. 📚🧠 It’s not just about replication—it’s about understanding the nuances behind policy design, reward optimization, and iterative learning loops. Thank you for sharing this resource— Abderahmane Boucetta, PhD can’t wait to dive deeper into the research! 🙌 #AI #ReinforcementLearning #MachineLearning #PolicyOptimization #AIResearch #OpenAI #Innovation 🚀📊

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