Why won't LLMs alone lead to AGI, and what architecture do we actually need?

**The Question:** So when people say that LLM will lead us to AGI how is that possible? Like in the base or the foundation, LLM is like a child who is just mugging up things and doesn't actually understand the information. He has just read all the books, will give you the answers, get full marks, but doesn't know how to apply or doesn't have actual knowledge of how things work. The child has just mugged up all the things, he doesn't understand anything. So I don't think it will lead us to AGI. We will need a different architecture. **Key Insights from the Conversation:** **The "Mugging Child" Problem:** LLMs are like giving a child the brain of a 50-year-old - full of information, memories, judgments, but the child hasn't lived. He hasn't experienced pain, choice, failure, love, risk. He can talk smart but doesn't understand the meaning. His consciousness can't grow because everything is already decided - no room for novelty, uncertainty, or becoming. **What's Missing:** - Embodiment (no senses, no physical experience) - Agency (they don't decide to pursue goals) - Situatedness (they don't live in the world) - True reasoning and causality understanding - The ability to evolve and adapt through experience **The System 1 + System 2 Solution:** LLMs are System 2 (slow, logical, deliberate) but lack System 1 (fast, intuitive, emotional, reactive). We need: • **System 2 (LLM):** Knowledge base, reasoning, language, memory • **System 1 (RL Agent):** Fast action, trial-error, instincts, environment learning **The Architecture:** System 1 (RL Agent) ↔ System 2 (LLM) [Fast action, adaptation] [Knowledge, planning] **Real-World Example - Gaming AGI:** - System 2 (LLM): Knows all game rules, strategies, tactics - System 1 (RL Agent): Learns by playing, explores, adapts, masters through experience - Together: Can both understand games conceptually AND excel at playing them **Technical Implementation:** - RL agent observes environment - When stuck/planning needed → queries LLM - LLM provides reasoning/strategy in structured format - Agent parses response → executes actions - Gets reward → updates policy (DQN, PPO) - Builds library of skills and experiences **Current Research:** - Voyager (Minecraft AGI using GPT-4 + RL) - ReAct (Reasoning + Acting) - Code-as-Policies - AutoGPT architectures **My Core Insight:** "LLMs are snapshots of intelligence. AGI will be a journey of intelligence. We don't need to just scale models - we need to create systems that can live, struggle, grow, and change. AGI won't be built, it will be grown." The first true AGI will likely be something we raise through experience, not something we train on data. It needs agents that learn by acting in the world using RL as their soul, with LLMs as their knowledge base.