PoEM: Predicting RL Outcomes from Existing Policies
Foundation models are post-trained with reinforcement learning (RL) to maximize specific rewards, such as human alignment, correctness, or instruction following. This post-training process is computationally intensive, sometimes unstable, and has to be run from scratch every time the reward model changes or when we want to combine multiple rewards. We hence ask: given a new reward function, is it…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
