Yucheng Shi on X: "Static datasets give fixed samples. Code-generated environments give scalable worlds. For LLM reasoning, executable environments can generate fresh problems and verifiable rewards. With TRON, we bring this idea to visual reasoning: each code-generated environment samples a https://t.co/H8C6eD3nR4" / X

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To view keyboard shortcuts, press question mark View keyboard shortcuts Home Explore Notifications Chat Grok Premium Money Bookmarks Creator Studio Articles Profile More Post christina @luoluo Post See new posts Conversation Yucheng Shi @Yucheng__Shi Static datasets give fixed samples. Code-generated environments give scalable worlds. For LLM reasoning, executable environments can generate fresh problems and verifiable rewards. With TRON, we bring this idea to visual reasoning: each code-generated environment samples a latent visual state, renders an image, asks a question, and verifies the answer from the underlying state. The name is inspired by the sci-fi film TRON: a virtual world created entirely by code, where agents can enter, interact, and evolve. This is not just about generating more data. It is about programming the distribution that produces data. Such environments make visual training data controllable, difficulty-scalable, and online, properties that are hard to obta

Yucheng Shi @Yucheng__Shi Static datasets give fixed samples. Code-generated environments give scalable worlds. For LLM reasoning, executable environments can generate fresh problems and verifiable rewards. With TRON, we bring this idea to visual reasoning: each code-generated environment samples a latent visual state, renders an image, asks a question, and verifies the answer from the underlying state. The name is inspired by the sci-fi film TRON: a virtual world created entirely by code, where agents can enter, interact, and evolve. This is not just about generating more data. It is about pr

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