Rethinking Reflection in Pre-Training

Key points

There is a common belief that complex cognitive abilities such as reflection in large language models emerge mainly during post-training stages like fine-tuning and reinforcement learning.
There is no systematic, scalable method for measuring reflection across different stages of pretraining.
Benchmarking reflection with standard reasoning datasets is difficult because reflective behavior is rare and error patterns are diverse.
To provide a finer-grained measurement framework, it defines multiple dimensions of reflection: situational, self, explicit, and implicit.
To systematically elicit and measure reflection by introducing human-like errors into reasoning chains, it develops a programmatic method for generating adversarial chain-of-thought datasets.
It uses a prompt-based LLM classifier to detect explicit reflection and measures four key metrics: accuracy, explicit reflection rate, explicit reflection accuracy, and implicit reflection accuracy.

External research summaries. These are not HDATF publications or measured product results.