Edwin Chen: Why Frontier Labs Are Diverging, RL Environments & Developing Model Taste

Foundation model development currently suffers from a reliance on flawed benchmarks that incentivize superficial metrics like verbosity and formatting over genuine accuracy. Optimizing for public leaderboards such as LLM Arena frequently results in models that prioritize "clickbait" responses rather than complex problem-solving. To achieve true progress, labs must shift toward rigorous human evaluation and "street smarts," ensuring models can handle the messiness of real-world tasks. The industry is moving toward RL Environments, which require rich, simulated worlds and high-quality human data to train agents effectively. Rather than a single "one-size-fits-all" model, the future lies in a constellation of models shaped by specific product theses—such as consumer engagement versus enterprise productivity—necessitating that organizations eventually train their own models to align with their unique goals and operational requirements.