Sample Complexity of Autoregressive Reasoning: Chain-of-Thought vs. End-to-End

Researchers have developed a framework to study how large language models learn to generate text autoregressively, focusing on the sample complexity under different types of supervision—End-to-End versus Chain-of-Thought. This work reveals that while End-to-End learning's sample complexity can vary widely with generation length, Chain-of-Thought supervision keeps it constant regardless of sequence length, significantly improving learnability for long sequences.

Read the full article at arXiv cs.LG (ML)

---

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.