When Errors Can Be Beneficial: A Categorization of Imperfect Rewards for Policy Gradient
Training language models via reinforcement learning often relies on imperfect proxy rewards, since ground truth rewards that precisely define the intended behavior are rarely available. Standard metrics for assessing the quality of proxy rewards, such as ranking accuracy, treat incorrect rewards as strictly harmful. In this work, however, we highlight that not all deviations from the ground truth are equal. By theoretically analyzing which outputs attract probability during policy gradient optimization, we categorize reward errors according to their effect on the increase in ground truth reward. The analysis establishes that reward errors, though conventionally viewed as harmful, can also...
| Authors | Shuning Shang, Hubert Strauss, Stanley Wei et al. |
|---|---|
| Primary category | cs.LG |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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