Published on November 5, 2025, the article explains how noisy feedback and complex infrastructure can destabilize reinforcement learning (RL) training.
In a publication dated November 5, 2025, the author argues that supervised fine-tuning (SFT) and reinforcement learning (RL) may share a form of loss function but differ in practice. RL depends on noisy feedback and more complex infrastructure for rollouts, log-probabilities, and reward shaping, factors that can destabilize training.
The article highlights data-quality and infrastructure risks that engineers should monitor in RL systems. To verify the argument and its details, consult the original publication and check the definitions and evidence it presents; the available summary specifies no measurements or quantitative results.