Reward Functions in AI: Between Rigidity and Adaptability

The relationship between human and artificial reasoning reveals an interesting tension in reward function design. While the human brain features a remarkably flexible reward system through its limbic system, current AI architectures rely on more rigid reward structures – and this might not be entirely negative.

Consider O1’s approach to reasoning: it receives rewards for both correct reasoning steps and achieving the right outcome. This rigid reward structure intentionally shapes the model toward step-by-step logical reasoning. It’s like having a strict but effective teacher who insists on showing your work, not just getting the right answer.

A truly adaptive reward system, similar to human cognition, would operate differently. It could:

• Dynamically focus attention on verifying individual reasoning steps
• Shift between prioritizing logical rigor and other objectives (elegance, novelty, clarity)
• Adjust its success criteria based on context
• Choose when to prioritize reasoning versus other goals

However, this comparison raises an important question: Is full reward function adaptability actually desirable? The alignment problem – ensuring AI systems remain aligned with human values and interests – suggests that allowing models to modify their own reward functions could be risky. O1’s rigid focus on reasoning steps might be a feature, not a bug.

The human limbic system’s flexibility is both a strength and a weakness. While it allows us to adaptively respond to diverse situations, it can also lead us to prioritize immediate satisfaction over logical rigor, or novelty over accuracy. O1’s fixed reward structure, in contrast, maintains a consistent focus on sound reasoning.

Perhaps the ideal lies somewhere in between. We might want systems that can flexibly allocate attention and adjust their evaluation criteria within carefully bounded domains, while maintaining rigid alignment with core objectives like logical consistency and truthfulness. This would combine the benefits of adaptive assessment with the safety of constrained optimization.