Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning

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