Or to write it less pessimistically, the models are trained to prime their own context window such that by the end of the chain they arrive at more valuable responses. By creating intermediary steps in the chain, the next step is easier to generate rather than moving directly to the desired response. We call it reasoning because it is intuitively analogous to human reasoning methods though it is understood that LLMs don't succeed as generally as humans are able to.