LatestMachine LearningInvestigating Transformer Attention and Reinforcement Learning Dynamics Using SelfGenerated Structural Data 0 like February 14, 2025Share this postLast Updated on February 14, 2025 by Editorial TeamAuthor(s): Shenggang Li Originally published on Towards AI. Cracking the Code: Synthetic Data as the Key to Understanding and Enhancing LLMsThis member-only story is on us. Upgrade to access all of Medium.Photo by Joshua Sortino on UnsplashBuilding large language models (LLMs) can be an endless battle against noisy, messy data. But what if we could strip away that noise and experiment in a clean, controlled environment? Thats exactly what we achieve with synthetic data structured token sequences like A, B, and ACB, designed to mimic relationships between words in NLP. So, we can explore and refine core LLM mechanisms without getting lost in real-world complexities.At the heart of this study are Multi-Head Latent Attention (MLA) and Group Relative Policy Optimization (GRPO), two powerful techniques inspired by DeepSeek. MLA optimizes how attention is distributed across tokens, while GRPO adjusts attention dynamically based on feedback, ensuring that critical tokens receive more focus. For instance, a token sequence like ACB isnt just processed linearly; GRPO learns which tokens to prioritize based on their impact on predictions.This project builds on AlphaGos strategies, where Monte Carlo Tree Search (MCTS) and reinforcement learning refined decision-making. I apply a similar idea to LLMs, using multi-path exploration to let multiple token contexts evolve simultaneously. Reinforcement learning then picks the best paths, cutting down on data needs while Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post