RL training: data and infrastructure risks
Published on November 5, 2025, the publication argues that noisy feedback and complex infrastructure can make RL training unstable.
Source: Por que o treinamento com RL pode ser mais frágil que o SFT (zhuanlan.zhihu.com). Text prepared with AI from this source.
What happened and what to do
On November 5, 2025, the publication argued that although SFT and RL can share a form of loss function, they differ in practice. RL relies on noisy feedback and more complex infrastructure for rollouts, log probabilities, and reward shaping; data quality issues and problems in these components can destabilize training.
A company training models with RL can implement data quality checks and monitoring for rollouts, log probabilities, and rewards. Alerts and dashboards can help detect deviations and infrastructure failures before they undermine experiments.
How the consultancy can help
Wendelmaques can assess risks in the data flow and training infrastructure, define a monitoring scope, and implement controls, dashboards, and operational routines suited to the company’s environment.
Next step
Send a short description of your RL training case to receive a scoped proposal.
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