arXiv:2605.24749v1 Announce Type: cross Abstract: Reward modeling is not only a prediction problem: in KL-regularized policy optimization, the learned reward is exponentiated to define the deployed policy, so downstream value depends on errors in reward-tilted regions. We study this feedback in a Gaussian single-index model with $r^*(x) = \sigma^*(\langle \theta^*, x\rangle)$ and $x \sim N(0, I_d)$. We analyze a two-stage neural reward model that first learns the hidden direction $\theta^*$ from reward-weighted samples and then fits the readout layer by weighted ridge regression. Exponential r
This research provides a deeper theoretical understanding of how neural reward models function in policy optimization, which is critical as AI agents become more sophisticated and deployed in complex environments.
Understanding the learning mechanisms of reward models is crucial for developing more robust, reliable, and interpretable AI systems, particularly for autonomous agents where reward function design is paramount.
This theoretical analysis offers insights into why current reward models perform as they do and provides a basis for designing more effective and predictable learning architectures for AI policy optimization.
- · AI researchers
- · Reinforcement learning developers
- · AI ethics and safety organizations
- · Developers of ad-hoc reward models
- · Systems with brittle reward functions
Improved design principles for reward functions in reinforcement learning will emerge.
More reliable and less 'surprising' AI agents will become possible, decreasing development timelines and increasing deployment safety.
This could accelerate the integration of AI agents into critical infrastructure and complex decision-making processes, given enhanced trust in their underlying learning mechanisms.
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Read at arXiv cs.LG