This paper investigates whether giving a language model extra information, such as a worked solution, improves its learning through on-policy self-distillation. A practitioner might care about how to optimize this technique for better performance.
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Researchers at Good Start Labs found that training AI models on games like Diplomacy and 1830: The Game of Railroads and Robber Barons can improve their performance on real-world tasks, such as customer support and financial research, by leveraging the strategic thinking and decision-making skills learned in the games. The training design, including the use of reinforcement learning environments and expert models, plays a crucial role in transferring these skills to the real world. AI summary
Carina Hong, CEO of Axiom Math, discusses the company's recent $200M Series A funding and their perfect Putnam exam score, highlighting their mission to scale "verified AI" through formal mathematics. She explains how formal verification, u…
Formal verificationAI for mathLean proof assistantMathematical discoveryPutnam examHardware verificationSoftware verificationRecursive self-improvementAI agentsTransfer learningAuto-formalizationCode generationComputational complexityReinforcement learningKnowledge graphsInterdisciplinary teamsStartup funding