Author

P. Shojaee

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#artificial intelligence Preprint Nov 2026

On the Primacy Bias in RLVR Training

It is found that although hard problems lead to modest gains overall, they are actually the ones that help most in expanding the model's reasoning capabilities and pushing the representations, which helps to better understand some of the prior conflicting observations about RLVR.

Phuc Minh Nguyen, Ngoc-Hieu Nguyen, P. Shojaee et al. · 0 citations

#machine learning May 2026

Why Do Reasoning Models Lose Coverage? The Role of Data and Forks in the Road

This paper investigates the cause of reasoning shrinkage under SFT-based post-training and identifies data-centric factors as a key driver of shrinkage in reasoning models and highlights diversity-aware designs as an effective lever for controlling it.

N. Nguyen, P. Shojaee, Phuc Minh Nguyen et al. · arXiv.org · 1 citation