PoEM: Predicting RL Outcomes from Existing Policies

Kimia Hamidieh, Giannis Daras, Antonio Torralba1 min readarXivSep 24, 2026

Key Takeaway for Students

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 rew

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This paper addresses the significant computational cost and instability associated with RL post-training for foundation models. By proposing a method to predict RL outcomes without running full RL, it offers a path to more efficient and stable model alignment, which is crucial for deploying robust AI systems.

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Career Insight

high demand

Why it matters

Skills to learn

Understanding of Reinforcement Learning from Human Feedback (RLHF) mechanismsFamiliarity with reward modeling techniques in LLMsConcepts of policy optimization and value functions in RLLinear algebra for combining reward functions and policiesEvaluation metrics for aligned language models

Interview questions

Explain the computational challenges of RLHF and how PoEM attempts to mitigate them.
If a new reward function is not a simple linear combination of existing ones, how might PoEM's approach need to be adapted or extended?
Design a system for efficiently managing and applying multiple reward models to a foundation model, considering the insights from PoEM regarding predicting RL outcomes.

Relevant for ~18 months · generated by the enrichment pipeline