arXiv:2606.03238v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) makes large-scale post-training possible by replacing an underspecified human objective with learned and scalable proxies. The same substitution creates a structured failure surface: optimization can raise the learned reward while external quality falls, degrade both proxy and judge scores, reveal proxy under-alignment, or produce evaluator-specific disagreement. We present an empirical failure-mode study of a compact RLHF pipeline with proximal policy optimization (PPO), direct preference optimiz
The paper is a new arXiv publication, addressing critical failure modes in RLHF as the technology becomes more prevalent in large language model development.
Understanding the failure modes of RLHF is crucial for the safe and reliable deployment of advanced AI systems, directly impacting their commercial viability and societal integration.
This research provides a mechanistic taxonomy, offering a structured framework to anticipate and mitigate issues like reward hacking and evaluator gaming in AI training, which can lead to more robust AI development processes.
- · AI safety researchers
- · Developers of robust AI systems
- · AI ethics and governance organizations
- · AI developers ignoring safety
- · Companies relying on poorly aligned AI
- · Rapid deployment of unscrutinized AI
Increased focus on advanced alignment techniques beyond basic RLHF.
Development of new tooling and methodologies specifically designed to detect and prevent reward hacking and gaming.
Slower, more cautious deployment of certain AI applications until these failure modes are better understood and mitigated, potentially influencing regulatory frameworks.
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Read at arXiv cs.LG