Investigating Transformer Attention and Reinforcement Learning Dynamics Using Self‑Generated Structural Data

Author(s): Shenggang Li

Originally published on Towards AI.

Cracking the Code: Synthetic Data as the Key to Understanding and Enhancing LLMs

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Building 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? That’s 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” isn’t just processed linearly; GRPO learns which tokens to prioritize based on their impact on predictions.

This project builds on AlphaGo’s 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.

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Published via Towards AI