REBEL: A Reinforcement Learning RL Algorithm that Reduces the Problem of RL to Solving a Sequence of Relative Reward Regression Problems on Iteratively Collected Datasets

Initially designed for continuous control tasks, Proximal Policy Optimization (PPO) has become widely used in reinforcement learning (RL) applications, including fine-tuning generative models. However, PPO’s effectiveness relies on multiple heuristics for stable convergence, such as value networks and clipping, making its implementation sensitive and complex. Despite this, RL demonstrates remarkable versatility, transitioning from tasks like […]

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