An Imperfect Verifier is Good Enough: Learning with Noisy Rewards

This project is a research artifact (based on an arXiv paper) addressing the critical 'post-training' bottleneck for reasoning models like OpenAI o1 or DeepSeek-R1. Its primary contribution is quantifying how much 'noise' (errors in the verifier or reward model) an RL agent can tolerate before performance degrades. While theoretically valuable, it lacks defensive moats; the findings are likely already known or currently being discovered internally by frontier labs (OpenAI, Anthropic, Google) who are the primary users of RLVR at scale. The repository's low traction (0 stars) suggests it is currently an academic reference rather than a utilized library. Its insights will likely be absorbed into major RLHF frameworks like TRL, OpenRLHF, or internal proprietary pipelines within months, rendering the specific code implementation obsolete. The value lies in the 'recipe' and experimental data, not the software itself.