Stabilizing reinforcement learning with LLMs
A Qwen team article published on December 16, 2025, examines instability in reinforcement learning for language models and highlights differences between training and inference, old and new policies, importance sampling, and routing replay.
Source: Estabilizando o reinforcement learning com LLMs (arxiv.org). Text prepared with AI from this source.
What happened and what to do
On December 16, 2025, the Qwen team published an article about instability in reinforcement learning for large language models. It highlights differences between training and inference and between old and new policies, and mentions importance sampling and routing replay as practices related to stabilization.
A company that trains or evaluates models could implement logging and monitoring to compare policies and track discrepancies between training and inference. A diagnosis could also assess where techniques such as importance sampling and routing replay fit into the existing process, without assuming they suit every scenario.
How the consultancy can help
Wendelmaques can diagnose a training and evaluation process, map instability risks, and propose a scoped implementation of instrumentation, monitoring, and workflow adjustments. Ongoing operation can also be included in the scope according to the company’s needs.
Next step
Send a short description of your model training or evaluation case to receive a scoped proposal.
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