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LED explores intermediate layers to broaden sampling
Published on July 10, 2026, the report presents Latent Exploration Decoding (LED), a method requiring no additional training that uses intermediate-layer distributions to improve sampling in reasoning models.
The article reports that post-training with reinforcement learning reduces the entropy of the final-layer distribution, while intermediate layers retain higher entropy. Latent Exploration Decoding (LED) uses these intermediate distributions to improve sampling in reasoning models, without additional training, with a focus on evaluating gains in pass@n.
To verify the result, consult the original article and examine how it defines and measures pass@n, which models and settings it tests, and how it compares LED with baseline sampling. The available summary gives none of those details and does not quantify gains; it therefore does not establish that the approach improves every model or task.
Original source: LED usa a diversidade das camadas intermediárias para recuperar a exploração