Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See

Researchers demonstrate that reward design fundamentally shapes how reinforcement learning agents allocate attention in autonomous driving tasks, with agents trained on different reward configurations exhibiting dramatically different focus patterns—up to 4.7x variation in attention to navigation tokens. The study validates attention analysis as a diagnostic tool for verifying that reward functions produce intended safety-critical behavior in RL systems.

This research addresses a critical gap in autonomous driving safety by demonstrating that reward function design directly influences not just agent behavior, but the internal cognitive mechanisms agents develop. The study uses three identically-architected agents trained on identical data with only reward configuration differences, isolating causality between incentive structure and attention patterns. This methodological rigor matters because autonomous driving systems operate in safety-critical environments where understanding decision-making mechanisms is paramount.

The findings reveal counterintuitive dynamics: agents with navigation rewards allocate vastly more attention to GPS-path tokens, while those trained with proximity penalties develop a 'learned vigilance prior'—maintained elevated surveillance even during collision-free phases. Most striking, different reward configurations sometimes create opposite attention-risk correlations, suggesting reward design doesn't merely modulate focus intensity but fundamentally reshapes what the system considers important. The researchers validate their methodology through Fisher z-transform aggregation, establishing that naive statistical approaches significantly underestimate attention-risk relationships.

For the autonomous driving and AI safety industries, this work provides practical diagnostic capabilities. Rather than treating trained models as black boxes, engineers can now analyze attention patterns to verify whether reward functions produce intended behaviors before deployment. This becomes increasingly important as autonomous systems scale, enabling more precise alignment between designed incentives and actual decision-making processes. The research suggests that attention analysis should become standard practice in RL validation pipelines, particularly for safety-critical applications where internal consistency between objectives and execution directly impacts real-world outcomes.

• →Reward design causes up to 4.7x variation in agent attention allocation, demonstrating direct causality between incentives and internal representations
• →Agents with time-to-collision penalties develop 'learned vigilance'—maintaining elevated surveillance even during safe driving phases
• →Different reward configurations can reverse attention-risk correlations entirely, not merely modulate their magnitude
• →Attention pattern analysis provides practical diagnostic validation for safety-critical RL systems before deployment
• →Proper statistical methodology (Fisher z-transform aggregation) reveals attention-risk relationships that naive pooling substantially underestimates