Musk: Steve, the real question I keep asking the team is whether today’s LLMs can reason when they leave the training distribution. Everyone cites chain-of-thought prompts, but that could just be mimicry. Hsu: Agreed. The latest benchmarks show that even Grok4-level models degrade sharply once you force a domain shift — the latent space just doesn’t span the new modality. Musk: So it’s more of a coverage problem than a reasoning failure? Hsu: Partly. But there’s a deeper issue. The transformer’s only built-in inductive bias is associative pattern matching . When the prompt is truly out-of-distribution—say, a symbolic puzzle whose tokens never co-occurred in training—the model has no structural prior to fall back on. It literally flips coins. Musk: Yet we see emergent “grokking” on synthetic tasks. Zhong et al. showed that induction heads can compose rules they were never explicitly trained on. Doesn’t that look like reasoning? Hsu: Composition buys you limited generalization, but the rules still have to lie in the span of the training grammar. As soon as you tweak the semantics—change a single operator in the puzzle—the accuracy collapses. That’s not robust reasoning; it’s brittle interpolation. Musk: Couldn’t reinforcement learning fix it? DRG-Sapphire used GRPO on top of a 7 B base model and got physician-grade coding on clinical notes, a classic OOD task. Hsu: The catch is that RL only works after the base model has ingested enough domain knowledge via supervised fine-tuning. When the pre-training corpus is sparse, RL alone plateaus. So the “reasoning” is still parasitic on prior knowledge density. Musk: So your takeaway is that scaling data and parameters won’t solve the problem? We’ll always hit a wall where the next OOD domain breaks the model? Hsu: Not necessarily a wall, but a ceiling. The empirical curves suggest that generalization error decays roughly logarithmically with training examples . That implies you need exponentially more data for each new tail distribution. For narrow verticals—say, rocket-engine diagnostics—it’s cheaper to bake in symbolic priors than to scale blindly. Musk: Which brings us back to neuro-symbolic hybrids. Give the LLM access to a small verified solver, then let it orchestrate calls when the distribution shifts. Hsu: Exactly. The LLM becomes a meta-controller that recognizes when it’s OOD and hands off to a specialized module. That architecture sidesteps the “one giant transformer” fallacy. Musk: All right, I’ll tell the xAI team to stop chasing the next trillion tokens and start building the routing layer. Thanks, Steve. Hsu: Anytime. And if you need synthetic OOD test cases, my lab has a generator that’s already fooled GPT-5. I’ll send the repo. This conversation with Elon might be AI-generated.