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Language models are increasingly doing their real work in the "invisible" latent space, not the tokens we see.
LLM agents can learn to elicit crucial information from users by rewarding interaction turns that most reduce uncertainty about the optimal action, leading to better task performance.
Forget scraping messy real-world websites: AutoWebWorld lets you synthesize infinite, perfectly verifiable web interaction data for just $0.04 a pop, dramatically boosting agent performance.
