Hongyang R. Zhang

Multi-step reasoning errors amplify exponentially with step count, but transformers can be trained to achieve stable, near-zero inference loss over arbitrarily long chains.

MoPLEx achieves up to 43.7% improvement in clustering accuracy by effectively learning from complex multi-way rankings, revealing the power of leveraging language models for preference optimization.