2 posts tagged with "evaluation"

If your model can solve a problem in 8 tries, RLVR trains it to succeed in 1 try. Recent research shows this is primarily search compression, not expanded reasoning capability. Training concentrates probability mass on paths the base model could already sample.

This matters because you need to measure what you're actually getting. Most RLVR gains come from sampling efficiency, with a smaller portion from true learning. This guide covers when RLVR works, three critical failure modes, and how to distinguish compression from capability expansion.

Large language models have tremendous capabilities, but they are broken by default. A wealth of open-source datasets has emerged to train and evaluate LLMs on safety, toxicity, and bias.

Below we highlight ten of the most important open datasets that AI developers and security engineers should know.