MaxRL: Efficient Maximum Likelihood for LLMs
Автор: AI Research Roundup
Загружено: 2026-02-09
Просмотров: 24
Описание: In this AI Research Roundup episode, Alex discusses the paper: 'Maximum Likelihood Reinforcement Learning' Maximum Likelihood Reinforcement Learning (MaxRL) is a new framework designed to bridge the gap between standard reinforcement learning and exact maximum likelihood optimization. Traditional RL methods often optimize only a lower-order approximation of the likelihood of correct rollouts in tasks like coding or math. MaxRL addresses this by introducing a compute-indexed objective that allows for better scaling as more sampling compute is allocated. Empirically, the method achieves up to 20x gains in test-time scaling efficiency compared to GRPO-trained models. These results suggest that MaxRL is a powerful new tool for training models in settings where correctness is the primary goal. Paper URL: https://arxiv.org/pdf/2602.02710 #AI #MachineLearning #DeepLearning #MaxRL #ReinforcementLearning #LLM #GRPO #CodingModels
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