Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning
Best AI papers explained - Podcast tekijän mukaan Enoch H. Kang - Torstaisin
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The paper optimizes test-time compute as a meta-reinforcement learning problem It emphasizes balancing exploration and exploitation to minimize cumulative regret Meta Reinforcement Fine-Tuning (MRT) improves performance and token efficiency
