Reward Errors in Policy Gradient Can Be Beneficial
A new theoretical analysis challenges the assumption that all reward errors harm reinforcement learning. The study categorizes errors in proxy rewards used for policy gradient optimization, showing that some deviations from ground truth are benign or even beneficial by preventing stagnation at mediocre outputs. Practical implications for RLHF are discussed.
Key facts
- arXiv:2604.25872v1
- Announce Type: cross
- Abstract: Training language models via reinforcement learning often relies on imperfect proxy rewards
- Standard metrics like ranking accuracy treat incorrect rewards as strictly harmful
- The work highlights that not all deviations from ground truth are equal
- The analysis categorizes reward errors according to their effect on ground truth reward increase
- Reward errors can be benign or beneficial by preventing policy from stalling around mediocre outputs
- Practical implications for reinforcement learning from human feedback (RLHF) are presented
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