SupportVectors: Generative AI, LLMs, Machine Learning

This week, we will walk through and discuss the paper:
LLM Post-Training: A Deep Dive into Reasoning Large Language Models
[https://arxiv.org/abs/2502.21321]

Abstract of the paper:
Large Language Models (LLMs) have transformed the natural language processing landscape and brought to life diverse applications. Pretraining on vast web-scale data has laid the foundation for these models, yet the research community is now increasingly shifting focus toward post-training techniques to achieve further breakthroughs. While pretraining provides a broad linguistic foundation, post-training methods enable LLMs to refine their knowledge, improve reasoning, enhance factual accuracy, and align more effectively with user intents and ethical considerations. Fine-tuning, reinforcement learning, and test-time scaling have emerged as critical strategies for optimizing LLMs performance, ensuring robustness, and improving adaptability across various real-world tasks. This survey provides a systematic exploration of post-training methodologies, analyzing their role in refining LLMs beyond pretraining, addressing key challenges such as catastrophic forgetting, reward hacking, and inference-time trade-offs. We highlight emerging directions in model alignment, scalable adaptation, and inference-time reasoning, and outline future research directions.

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We are a group of applied AI practitioners and enthusiasts who have formed a collective learning community. Every Wednesday evening at PM PST, we hold our research paper reading seminar covering an AI topic. One member carefully explains the paper, making it more accessible to a broader audience. Then, we follow this reading with a more informal discussion and socializing.

We are a group of applied AI practitioners and enthusiasts who have formed a collective learning community. Every Wednesday evening at PM PST, we hold our research paper reading seminar covering an AI topic. One member carefully explains the paper, making it more accessible to a broader audience. Then, we follow this reading with a more informal discussion and socializing.

This is a free four-session course designed to demystify modern artificial intelligence's core concepts. This course aims to equip you with a solid foundation in machine learning.

In today's data-driven world, machine learning algorithms are the engines powering innovations across industries. From recommendation systems and fraud detection to autonomous vehicles and medical diagnostics, ML/AI technologies are reshaping our world. This course will give you the tools to understand and harness these powerful techniques.

Our approach is rooted in intuition. Rather than drowning in mathematical formulas, we'll build your understanding through carefully chosen examples and analogies. You'll learn to think like a machine learning practitioner, understanding not just the 'how,' but the crucial 'why' behind each concept.

We are a group of applied AI practitioners and enthusiasts who have formed a collective learning community. Every Wednesday evening at PM PST, we hold our research paper reading seminar covering an AI topic. One member carefully explains the paper, making it more accessible to a broader audience. Then, we follow this reading with a more informal discussion and socializing.