JustRL tests simple RL on 1.5B reasoning models
Published on January 4, 2026, the paper describes single-stage training with fixed hyperparameters and reports average accuracies of 54.9% and 64.3% across two 1.5B reasoning models.
Source: JustRL testa uma receita simples de RL em modelos de raciocínio de 1,5B (arxiv.org). Text prepared with AI from this source.
What happened and what to do
The paper presents JustRL, a single-stage reinforcement-learning training recipe with fixed hyperparameters, tested on two reasoning models with 1.5 billion parameters. Published on January 4, 2026, it reports average accuracies of 54.9% and 64.3%, and proposes the approach as a baseline for comparison with more complex recipes.
A company evaluating model training could implement a reproducible benchmark: track data, configurations, and metrics, run the simple recipe as a baseline, and compare its results with more elaborate alternatives. This supports evidence-based training decisions without assuming that one recipe fits every use case.
How the consultancy can help
Wendelmaques can diagnose the opportunity, define a benchmark and evaluation scope, and implement and operate an experiment pipeline, configuration tracking, and metric monitoring on the company's infrastructure.
Next step
Send a short description of your company's model, data, and evaluation goal to receive a scoped proposal.
Let's assess your project
I reply with scope and a proposal.