Rethinking Reflection in Pre-Training

Essential AI; :; Darsh J Shah; Peter Rushton; Somanshu Singla; Mohit Parmar; Kurt Smith; Yash Vanjani; Ashish Vaswani; Adarsh Chaluvaraju; Andrew Hojel; Andrew Ma; Anil Thomas; Anthony Polloreno; Ashish Tanwer; Burhan Drak Sibai; Divya S Mansingka; Divya Shivaprasad; Ishaan Shah; Karl Stratos; Khoi Nguyen; Michael Callahan; Michael Pust; Mrinal Iyer; Philip Monk; Platon Mazarakis; Ritvik Kapila; Saurabh Srivastava; Tim Romanski

arXiv · 2504.04022

Rethinking Reflection in Pre-Training

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Abstract

A language model's ability to reflect on its own reasoning provides a key advantage for solving complex problems. While most recent research has focused on how this ability develops during reinforcement learning, we show that it actually begins to emerge much earlier - during the model's pre-training. To study this, we introduce deliberate errors into chains-of-thought and test whether the model can still arrive at the correct answer by recognizing and correcting these mistakes. By tracking performance across different stages of pre-training, we observe that this self-correcting ability appears early and improves steadily over time. For instance, an OLMo2-7B model pre-trained on 4 trillion tokens displays self-correction on our six self-reflection tasks.

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Essential AI, :, Darsh J Shah, Peter Rushton, Somanshu Singla, Mohit Parmar, Kurt Smith, Yash Vanjani, Ashish Vaswani, Adarsh Chaluvaraju, Andrew Hojel, Andrew Ma, Anil Thomas, Anthony Polloreno, Ashish Tanwer, Burhan Drak Sibai, Divya S Mansingka, Divya Shivaprasad, Ishaan Shah, Karl Stratos, Khoi Nguyen, Michael Callahan, Michael Pust, Mrinal Iyer, Philip Monk, Platon Mazarakis, Ritvik Kapila, Saurabh Srivastava, Tim Romanski. 2025-04-05. Rethinking Reflection in Pre-Training. https://arxiv.org/abs/2504.04022

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