From REINFORCE to GRPO: policy gradient methods for LLMs
An article published on September 24, 2026, presents a progression of policy gradient methods used to train LLMs, from vanilla policy gradient and REINFORCE to PPO, GRPO, and GRPO variants.
Source: Uma progressão de métodos de policy gradient para treinar LLMs (x.com). Text prepared with AI from this source.
What happened and what to do
The article, published on September 24, 2026, presents an evolution of policy gradient methods applied to LLM training: vanilla policy gradient, REINFORCE, PPO, GRPO, and GRPO variants. It also explains that REINFORCE estimates the policy gradient using sampled rollouts and offers engineers a concise conceptual map of these approaches.
A company evaluating reinforcement learning for training can turn this map into an experimentation plan: define tasks and evaluation criteria, record each method’s configuration and results, and track cost and quality per run. The comparison can inform a technical decision before a method is integrated into the training workflow.
How the consultancy can help
Wendelmaques can diagnose the opportunity, scope an experimentation pipeline, and implement data collection, evaluation, and dashboards to compare training methods. The proposal can also cover operating the solution.
Next step
Send a short description of your case to receive a scoped proposal for evaluating or implementing this experimentation workflow.
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