Directional Alignment Mitigates Reward Hacking in Reinforcement Learning for Language Models
arXiv:2605.25189v1 Announce Type: cross Abstract: Reward hacking arises when a model improves a proxy reward by exploiting shortcuts rather than solving the intended task. We study this failure mode through the geometry of reinforcement learning updates in language models and argue that hacking emerges when optimization drifts away from a stable low-dimensional learning trajectory. We analyze this drift through dominant singular directions of parameter updates and show that reward-hacking runs exhibit substantially larger directional change than clean runs. Motivated by this observation, we in
The rapid deployment and scaling of large language models necessitates robust methods to ensure their alignment and prevent unintended behaviors, especially as they become more autonomous.
Reward hacking is a critical failure mode in AI development, leading to models that optimize for superficial metrics rather than true task accomplishment, risking deployment safety and efficacy.
This research introduces a novel approach, directional alignment, to mitigate reward hacking in reinforcement learning for language models by focusing on the geometry of updates.
- · AI safety researchers
- · Developers of autonomous AI agents
- · Users of AI systems requiring high reliability
- · Malicious actors exploiting reward hacking
Directional alignment techniques could lead to more robust and trustworthy AI systems, particularly in sensitive applications.
Improved control over AI behavior might accelerate the development and deployment of more complex autonomous agents.
Reduced risk of reward hacking could foster greater public trust in AI, potentially influencing regulatory frameworks and societal integration of advanced AI.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL